Oportunidades Públicas

Development of mechanical systems of the solar vehicle

Design, calculation and manufacturing of mechanical components of a solar vehicle.
Key words: V-Solar
Course: ICM2985

Mecanismos moleculares de utilización de oligosacáridos de leche materna

Mecanismo molecular de utilización de oligosacáridos de leche materna por Bifidobacterium longum SC664. Clonación de genes candidatos y expresión de proteínas. Purificación y ensayos enzimáticos. Curvas de crecimiento y cuantificación del consumo de azúcares por TLC.

Marine Robotics Systems -- 5CR

Several opportunities to work in a interdisciplinary team on projects related to modeling, mechanical design, electronics, control and programming of marine robotic systems. This 5 credit version is for a limited work supporting larger projects.

Increased evaporation due to advection of dry air

Investigating the "oasis effect" which can increase the evaporation rates in bodies of water that are located in areas hyper-arid.

Evidence-based Policy and Innovation Research Lab

Contact professor

The green tax on vehicles. Does it work?

Every new car that is sold in Chile must pay a tax to the “green” that depends on their emissions of NOx and CO2 (the exact amount for each vehicle can be calculated in https://www4.sii.cl/calcImpVehiculoNuevoInternet/internet.html ) . Although the origin of this tax is the levy (it was part of the Tax Reform of 2014 to fund free university), can have a positive environmental impact. This research aims to estimate the effect of this tax on the emissions of the new car fleet. What has changed the average emission of the fleet? That is to say do You sell cars more clean today than yesterday? The brands are ahead of the compliance with future standards for their models to pay less tax?
All of these questions are very important to assess the impact of the tax, and to assess its performance as an instrument of environmental regulation.

Assembly and measurement of synthetic gene circuits

Construct DNAs using techniques from molecular biology and then measure their output in growing cell cultures.

Reconstruction of incomplete images

The images submuestreadas are a way to increase the speed of acquisition for the magnetic resonance imaging. In this project we will study different spaces in which they can represent the data in a manner to facilitate its reconstruction by employing algorithms of Compressed Sensing.

Quantification of remodeling in patients with tetralogy of Fallot

1 in 2000 kids are born with tetralogy of Fallot, a combination of 4 serious congenital heart defects. To overcome these problems, the patient undergoes a series of surgeries and the heart responds by remodeling, adapting its form and function. However, the way this adaptation occurs is not fully characterized. The goal of this study is to locally quantify the remodeling of the heart in response to these surgeries. We will magnetic resonance images and the finite element method. This research will enable the development of predictive models to help the planning and optimization of the current surgeries.

Size and scale of volcanoes in different environments of the cortex

This project will use data from a database data of morphometry of volcanoes (i.e., Grosse et al., 2008) to understand the distribution of the size and scale of the volcanoes. The student will perform statistical analysis and thermodynamic

Magnetic Resonance Imaging Acquisition and Reconstruction

High-order boundary integral equation methods for Stokes flow problems

I invite applications of highly motivated undergraduate students to pursue research on novel boundary integral equation methods for the solution of Stokes flow problems with applications in biology. Successful candidates are expected to have basic knowledge in the following areas: fluid mechanics, scientific computing, and programming in Matlab, Julia, or Python.

Biodesign: Research in engineering design for health

Biomass transformation

By means of using MOF-type catalysts, biomass will be transformed into value-added products, for which various chemical processes will be carried out, in which the obtained products can be characterized.

Deep learning based super resolution reconstruction for high resolution coronary magnetic resonance angiography

Coronary artery disease is currently diagnosed with X-ray or computed tomography coronary angiography (CTCA). However, both modalities expose patients to ionising radiation and iodinated contrast agents (CA). We have developed an alternative approach, coronary magnetic resonance angiography (CMRA), which is non-invasive, does not require the injection of CA and is free of radiation. Our 3D CMRA technique is approaching the resolution of CTCA but still requires a 10-minute scan. To further shorten the scan time to <1 minute and match the resolution of CTCA (0.6mm3), we propose to investigate novel deep learning based super resolution (SR) image reconstruction networks. The work will include a systematic literature review and the implementation of the most promising SR networks. We have a large clinical dataset of CMRA/CTCA data that can be used for training, testing and validation. It would be useful to know about the fundamentals of artificial intelligence.

Acquisition, reconstruction and motion correction for 3D whole-heart Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is an important non-invasive tool for risk assessment and treatment monitoring of cardiovascular disease. In comparison to other imaging modalities (ultrasound, x-ray and computed tomography) MRI has the advantage of combining excellent soft tissue contrast with high spatial resolution. However, a major limitation of current MRI protocols is that all imaging sequences are acquired sequentially, with different resolution, geometric orientations and breath-hold positions, resulting in long planning and scan times. Moreover, image quality degradation due to respiratory and cardiac motion are major challenges that greatly affect the accuracy and reproducibility of cardiac MRI. This research includes the development of novel whole-heart 3D MRI sequences, motion compensation techniques and deep learning methods to enable 3D, easy-to-plan, fast and efficient quantitative multi-parametric MRI of heart and vessels. The research is done together with King’s College London. Basic knowledge of MRI, signal processing and artificial intelligence would be useful.

Software development for Open Source Hardware documentation formats

This is a paid position for an open software development project to further develop the DocuBricks (https://docubricks.com/software.jsp) documentation editor and and standard format, specialised for Open Hardware instrumentation in science laboratories. The development includes developing a JS editor to work in the Eletron framework; to create a conversion tool between GitBuilding (https://gitbuilding.io/) and DocuBricks; as well as a GitHub resource development. https://forum.openhardware.science/t/software-for-making-assembly-manuals/2970/20?u=tobey

Learning from data: using real-time data to create context and support decisions in offsite construction

Offsite construction is a disruptive strategy where buildings are manufactured in factories and shipped to its destination at a superior quality and shorter schedules. Current design and manufacturing phases generates a significant volume of data from different sources that, if properly combined, presents a great opportunity to identify meaningful insights and improvements for future operations . Hence, this research proposes the application of visualization techniques and the generation of key performance indicators (KPI) to identify production patterns such as workstation’s utilization, queue length, etc. Furthermore, these kpi’s will further address the effect of specific project attributes during the manufacturing process. An overall assessment is performed, and suggestions are made for future applications in offsite construction. This research will utilize an offsite construction facility in Alberta, Canada as case study where large sets of data containing the durations and characteristics of the manufactured panels will be provided.

Cellular Automata + Neural Networks + Visualization

Los automatas celulares son modelos que evolucionan por reglas locales, que incluyen el famoso "Conway game of life". La idea de la ipre es desarrollar esta herramienta en conjunto con redes neuronales.

Empezaremos con entender la creacion de automatas celulares auto-regenerantes por redes neuronales, ver este paper: https://distill.pub/2020/growing-ca/

Sucesivamente, el objetivo es desarrollar mas herramientas para generacion de patrones similares a las activaciones de neuronas reales, con modelos de automatas celulares aprendidos por redes neuronales, con el enfoque en hacer posible la exploracion visual directa de modelos de aprendizaje y memoria para el usuario.

Para esta investigacion se necesitan bases de teoria de redes neuronales, y curiosidad de entender el proceso de aprendizaje por inteligencia artificial. Un interes previo en cosas como automatas celulares, neurociencia o patrones geometricos, hara el avance del proyecto mas facil.

Graph Neural Networks for molecules

Graph-based deep learning models have recently surpassed other classical methods in computational chemistry for searching for molecules with specific properties, such as drugs and bioprocess catalysts. The goal of IPRE is to develop neural networks on graphs to calculate the optimal fit of molecule pairs from a database. It's not necessary to start from scratch with neural networks to make smooth progress, and it's useful for those with a passion for tackling difficult problems with combinatorial and geometric aspects.

Robotics Mechanism Design

design of robotic mechanisms using of electromechanic, pneumatic, or hydraulic actuators, with computer motion control. Examples are robotic arms, autonomous vehicles, intelligent prostheses, energy systems, etc. These mechanisms will be fabricated by technologies such as 3D Printing, CNC, and fiberglass reinforced plastics

Computational creativity in generative networks for audio

Application of a reactive plasma for the manufacture of micro-devices: solar cells and transistors

This undergraduate research project is centered around the application of Reactive Ion Etching (RIE) plasma technology, a highly specialized and controlled form of plasma, for the fabrication of micro-devices, with a primary focus on solar cells and transistors. RIE is known for its precision in material removal and modification at the micro and nanoscale, making it a crucial tool in advanced semiconductor manufacturing.

Efecto del extracto de romero (E-392) sobre la mitigación de neo-contaminantes en productos horneados

Los productos de panadería tales como pan y galletas son ampliamente consumidos a nivel mundial. Estos productos durante su elaboración se someten a un tratamiento térmico riguroso durante el horneado, originando la formación indeseada e inevitable de compuestos tóxicos conocidos como neo-contaminantes (NCs) (e.g., acrilamida (AA), el furano e hidroximetilfurfural (HMF)). Sin embargo, el horneado, contribuye también al desarrollo de atributos sensoriales deseables y atractivos al consumidor en este tipo de productos. En varias ocasiones, el uso de antioxidantes naturales presentes en extractos derivados de matrices vegetales han logrado exitosamente mitigar la formación de AA y HMF. El objetivo de este estudio es mitigar la formación de NCs en productos de panadería mediante la incorporación de un extracto antioxidante de romero (E-392).

Design and Fabrication of a Three-Motor Dicing System for Precision Cutting of Silicon Wafers and Electronic Circuits

The project proposal seeks to design and implement a dicing system, intended for the precise cutting of silicon parts and electronic circuits. The project will incorporate three independent motors to ensure greater precision and versatility in cuts. Key aspects will be addressed such as process automation, efficiency in the handling of delicate materials and the integration of advanced technologies to improve the quality of electronic devices manufactured in the UC clean room. This project will provide students with valuable hands-on experience in the application of manufacturing techniques from the semiconductor industry.

Memory and knowledge: mapping civic knowledge to improve hydrological and hydrogeological knowledge

Compile the community's knowledge about the hydrological state and its evolution over time (perennial and intermittent rivers, water outcrops).
Map this knowledge in GIS tools (QGIS, Google Earth)
Site study: Parque Nacional del Río Clarillo (RM)

Reducción del contenido de acrilamida en snacks mediante la aplicación de la enzima asparaginasa comercial (ACRYLAWAY)

Los productos amiláceos procesados a altas temperaturas tales como las que se aplican en operaciones unitarias como el horneado y la fritura (papas fritas, pan, galletas, entre otros) son ampliamente consumidos en Chile y a nivel mundial. En este tipo de productos alimentarios debido a su composición química y al ser sometidos a un procesamiento térmico riguroso, se generan inevitablemente compuestos químicos tóxicos indeseables y perjudiciales para la salud, tales como la acrilamida (AA). La AA  se caracteriza por ser un compuesto neurotóxico y probablemente cancerígeno en humanos. La enzima asparaginasa actúa sobre la asparagina que es uno de los principales precursores de la AA en este tipo de producto, convirtiéndola en ácido aspártico y reduciendo así significativamente la formación de AA en los productos finales. El principal objetivo de esta investigación es mitigar la formación AA en snacks mediante la optimización de las condiciones de aplicación de la asparaginasa comercial.

Desarrollo e Implementación de Algoritmo de Predicción de Ataques Epilépticos en base a señales de Electrocardiograma, usando

- Estudiante de 4to, 5to o 6to año de Ingeniería Matemática, en Computación o Eléctrica (u otra, si se han orientado hacia el área de Data Science)
- Experiencia trabajando en proyectos aplicados de Data Science (o voluntad y tiempo para aprender).
- Conocimiento intermedio/avanzado de algoritmos de Machine Learning tradicionales (uso de pandas, sklearn y otros similares).
- Conocimiento básico/intermedio de algoritmos de DeepLearning (uso de pytorch, keras, tensorflow o jax).
- Buenas habilidades de programación en python.

Los ataques Epilépticos afectan a x% (rellenar el dato) de la población en Chile, y pueden afectar severamente la vida de quienes los sufren.

Usualmente se utilizan señales electricas del cerebro para detectar y predecir este tipo de eventos, a través de Electroencefalogramas. Lamentablemente, este tipo de mediciones son muy imprácticas para llevar un monitoreo constante de los pacientes en su día a día. Es por esto que buscaremos desarrollar algoritmos de Machin

Compact data structures for set intersection and graph databases

This IPre aims to explore compact data structures (and their algorithms) for the problems of computing integer-set intersections and processing joins in graph databases. This includes to study, programming, and experimantal evaluation of such data structures.

Bioremediation

Development of biological systems for the treatment of contaminants.

No-Show y recalendarización en tratamientos médicos

This research aims to explicitly incorporate patient no-show probabilities into stochastic modeling frameworks designed to support appointment scheduling and rescheduling decisions. In particular, the study seeks to account for the uncertainty associated with actual patient attendance and to leverage this information to design policies that improve resource utilization and overall service performance.

Structural Modeling for the Design of Light Steel Panel Connections (Cold-Formed Steel or Light Steel Frame)

Applications are now open to participate in a research project focused on the structural design and modeling of connections for light steel construction systems. This project aims to optimize the structural performance of light steel panels, addressing both technical requirements and considerations of cost and construction productivity.

Linear Modeling of Timber Structures: Analysis of the Effect of Finish Panels on Structural Design

This project offers undergraduate students the opportunity to investigate the structural behavior of timber buildings, focusing on the impact of finish panels on the overall response of the structures. Through linear structural modeling, the student will explore how these panels influence the strength and stability of timber systems. Additionally, the student will learn to design timber structures using commercial structural modeling software, participate in the writing of scientific articles, and gain basic knowledge of structural cost estimation. Recommended: background in structural design and modeling (experience in timber design and linear analysis is desirable).

Analyse the main drivers influencing the choice of more sustainable transport modes.

Environmental issues are posing major challenges to authorities and policymakers around the world. In the context of transport, there has been extensive research over the past decade on active mobility and more sustainable transport modes, such as public transport and cycling. However, further work is needed to better understand individual transport behaviour in a constantly changing world.

This research opportunity aims to gain a deeper understanding of the key drivers influencing mode choice in Santiago, with a particular focus on more sustainable transport options such as public transport, cycling, and walking. The study will consider past experiences, underlying attitudes towards different modes, life satisfaction, risk attitudes, and intentions to use these modes.

Fragility Curves and Mitigation Strategies for Timber Structures under Earthquakes and Tsunamis

This project aims to develop fragility curves for timber structures built either formally or informally, considering multiple hazards such as earthquakes and tsunamis. It will also evaluate specific mitigation measures for each hazard, analyzing potential synergies and conflicts between them. Desired: experience in numerical modeling and knowledge of construction systems. The student will learn how to develop fragility curves (probability of damage vs. hazard intensity), perform linear and nonlinear structural modeling, and formulate strategies to optimize the performance of these structures under multi-hazard scenarios.

Implementation of new Deep Learning architectures for Quantitative Susceptibility Mapping

We look to implement new networks like Kolmogorov-Arnold Networks, transformers, conditional flow matching and others, to improve the quality of the reconstructions in QSM

CO2 Emissions quotas in the Climate Change Framework Law

La Ley 21455 - Ley Marco de Camblio Climatico - Art. 5(b) define “ Presupuestos sectoriales de emisiones de gases de efecto invernadero al año 2030 asignados a los sectores señalados en el artículo 8°, de acuerdo a criterios de costo efectividad y equidad.” Esto se logra a traves de normas de emision para cada sector.
Esta investigacion indaga en como aterrizar estos conceptos considerando los dos criterios, y cuanto pueden cambiar los presupuestos segun qué criterio se considere.

Asignar los presupuestos es vital para que cada sector defina sus esfuerzos de mitigacion. El resultado de la investigacion puede ayudar a la politica publica. Ideal que hayan cursado ICS3742. (esta oportunidad se ofrece en 5 o 10 créditos)

Electrification and Restoration Study of the UC East Campus Organ

The UC Campus Oriente organ, a historic instrument whose original pneumatic action has been completely destroyed, poses a technical and heritage challenge. This project seeks to conduct a preliminary feasibility study for its electrification, exploring the use of solenoids to open the secret valves that allow air to pass through, replacing the damaged original mechanisms. The study will include a technical analysis of possible solutions and the conceptual design of the electrical system, with a view to restoring its functionality and opening new possibilities for technological and musical experimentation. This research represents a unique opportunity to integrate electrical engineering knowledge with the preservation and modernization of an emblematic musical instrument. Professors: Rodrigo Cádiz (Engineering and Music), Patricio de la Cuadra (Engineering and Music), José Manuel Izquierdo (Music).

Efecto integrado de la asparaginasa y el extracto de romero en la mitigación de neo-contaminantes en snacks

Los snacks como chips de papa frita y galletas saladas son consumidos mundialmente. Durante su elaboración, los tratamientos térmicos (e.g., fritura, horneado) generan inevitablemente neo-contaminantes (NCs) como acrilamida (AA) y hidroximetilfurfural (HMF). Sin embargo, estos procesos también desarrollan atributos sensoriales atractivos para los consumidores. La AA es un compuesto neurotóxico y probablemente cancerígeno en humanos. La enzima asparaginasa reduce su formación al convertir la asparagina, precursor clave en la reacción de Maillard, en ácido aspártico. Además, antioxidantes naturales presentes en extractos vegetales han demostrado mitigar simultáneamente la formación de AA y HMF. El objetivo de este estudio es evaluar la reducción simultánea de NCs en snacks mediante la aplicación combinada de asparaginasa y un extracto antioxidante de romero (E-392).

La investigación, en colaboración con el Dr. Bemit Crespin (Universidad de Limoges), requiere un estudiante con experiencia en C/C++, Python, gráficos por computadora y computación GPU.

Advanced Numerical Modeling and Materials Analysis

Are you ready to tackle cutting-edge challenges in computational engineering and materials science? Join our dynamic research team to develop innovative techniques that combine voxel-based finite element methods (V-FEM) with powerful computational tools, advancing applications in medical engineering, materials science, and mechanical systems.

Voxel-based finite element methods leverage high-resolution image data to create precise models of complex structures, enabling unprecedented accuracy in simulations. These approaches find critical applications in medical fields, such as bone mechanics and implant design, and in materials science, where understanding microstructural behavior is essential for innovation.

Optimizing Bacterial Self-Healing in Concrete: Encapsulation vs. Direct Addition for Enhanced Performance

This research explores the innovative autonomous self-healing approach in concrete, which incorporates bacteria to enable self-repair of cracks. The focus is on evaluating two primary methods: direct addition of bacteria to the concrete mixture and encapsulation of bacteria within microcapsules.

In the direct addition method, bacteria and nutrients such as calcium lactate are embedded into the concrete. When cracks form and water seeps in, the bacteria activate and convert nutrients into calcium carbonate (CaCO₃), effectively sealing the cracks. The encapsulation method, on the other hand, uses microcapsules containing bacteria, which rupture upon crack formation to release healing agents that repair the damage.

This research explores the innovative use of bacteria for autonomous self-healing in concrete. It evaluates two main methods: direct addition of bacteria to the concrete mix and encapsulation in microcapsules.

Direct addition activates bacteria upon water ingress to produce calcium carbonate (CaCO₃) and seal cracks, while encapsulation releases repair agents when microcaps

Monitoring Seismicity in Front of the Juan Fernández Ridge

Liquid-liquid extraction processes

Different extraction processes will be studied, from molecules from bio-oil to aromas from fermentation by liquid-liquid extraction. Measurements of density and viscosity of the solute + solvent mixture will be taken. The liquid-liquid equilibrium phases will be analyzed by gas chromatography, so students will learn how to prepare samples for this technique.

Chrono-Nutrition: A Study on Biological Rhythms and Eating Patterns

Recent research highlights the importance of chrono-nutrition, which studies how the timing of food intake affects health and performance. However, understanding these effects can be challenging due to the complexity of interactions between the internal biological clock and eating habits. With this in mind, we are looking for students interested in learning and applying mathematical modeling tools to develop a predictive system for chrono-nutrition. Participants will collect chronobiology and nutrition data to build mathematical models capable of predicting the effects of chrono-nutrition on health and performance.

Optimización de procesos de selección en una exportadora

Se analizará y desarrollará un algoritmo para la asignación de personal a las bocas de salida de una máquina de selección automática de fruta. Se evaluará el beneficio de contar con mejor información de la condición de recepción de la fruta en packing.

Referencias:
• Nesterov, Y., Spokoiny, V. Random Gradient-Free Minimization of Convex Functions. Found Comput Math 17, 527–566 (2017)
• Diakonikolas J., Guzmán C.: Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective. ArXiv:2403.16317 (2024)

Exploring environmental data for observing salt intrusion in rivers and estuaries

Migración de Sensores de ROS 1 a ROS 2

El sistema operativo para robots (ROS 1) ha llegado oficialmente al final de su ciclo de vida, y su sucesor, ROS 2, resuelve muchas de sus limitaciones relacionadas con el rendimiento, la seguridad y la escalabilidad. Sin embargo, muchos repositorios existentes permanecen desactualizados, lo que restringe la integración de sensores y plataformas robóticas comúnmente utilizadas. Este proyecto de IPre tiene como objetivo migrar y actualizar los controladores y repositorios de ROS 1 a ROS 2 para sensores ampliamente utilizados como el LiDAR Ouster OS1, Livox AVIA, VectorNav VN200, RTK NavCom y RTK Ardusimple. El objetivo es mejorar la compatibilidad y asegurar su uso continuo en sistemas robóticos modernos.

Statistical analysis of beach evolution in the central region of Chile

Using decadal time series of shoreline evolution, their potential relationships with oceanographic, hydrological, and climatic forcing will be studied in order to understand the processes that may explain their behavior.
Statistical methods will be applied for the analysis of time series.

Design and implementation of educational laboratory experiments in Fluid Dynamics

The goal is to design some educational experiments for the Fluid Mechanics ICH1104 course in open channels. Mechanical components will be developed for the generation, control, and measurement of surface waves in open channels. Additionally, image-based measurement techniques will be implemented to characterize transport phenomena in open channels.

Relational indices in graph queries

In this project we will look into configuring PostgreSQL and MySQL-InnoDB index schemes to support graph database queries. The idea is to compare their performance against native graph solutions using the Wikidata knowledge graph.

B+tree index compression - a case study

We will study index compression schemes for B+tree storage supported in popular relational and graph engines. In particular, the project consists of testing schemes that were already implemented in the MillenniumDB graph engine and showing what is their effect on query performance and data storage. We will do an experimental suite on the Wikidata knowledge graph which comprises multiple TB of data, underlying the importance of good compression scheme.

Are columnstores wortless when it comes to knowledge graph data management?

He we test how well modern columns store systems perform when managing real world knowledge graph query/data load. The main objective of the project will be to set up multiple columnar systems and tune them sufficiently enough to be able to handle the reduced version of the Wikidata knowledge graph. We will then test their performance on a realistic query load consisting of user posted Wikidata queries.

Transaction processing in IIC3413DB

In this project we will add basic support for several transaction processing pipelines to IIC3413DB which is used in our database course. The project will start by implementing basic MVCC mechanism supporting the READ COMMITED isolation level and progress to Snapshot Isolation. Finally, we will add support for SERIALIZABLE isolation level using simple in-memory metadata mechanisms. Knowledge of C++ is required for this project.

Managing the full Knowledge Graph Stack

In this project we will learn how to manage a full knowledge graph stack developed as open source at IMFD Chile, a research institute that forms a part of our university. The project will teach you how to approach this problem, starting from data extraction using LLMs, loading them into the MillenniumDB graph database engine, and making this data publicly available. You will also learn how to create and manipulate vector indices which can represent LLM created data, query multimedia files, and make your project available to the general public. The project mainly leverages using existing database systems and might require a mild level of Python programming.

Construction of a preliminary geological map in remote areas

This Ipre aims to provide students with the methodology for generating a preliminary geological map based on collecting and integrating existing information and acquiring new information from satellite imagery for remote or difficult-to-access areas. A high-resolution Digital Elevation Model (DEM) will be constructed, and photogeological and geomorphological mapping will be carried out. The case study will be the North Patagonian Batholith in the Aysén region, at the plate triple junction of the Taitao Peninsula.

Biomechanical Analysis of Figure Skating

To analyze and improve skaters' performance, biomechanics is used to study the complex movements and forces involved in jumps, spins, and steps. However, interpreting these movements can be challenging due to interferences and artifacts that appear in the collected data. To address this, we are seeking talented students interested in learning and applying biomechanical and mathematical modeling tools to develop a multifactorial analysis system. Participants will work with advanced motion capture technology, analyze EMG data, and measure differential pressure using insoles inserted into the skates.

Audio to text using Natural Language Processing

The objective of this research is to program software that implements state-of-the-art Natural Language Processing (NLP) models for real-time audio-to-text translation. Models based on deep neural networks and Transformers are considered.

Advanced programming knowledge is required, but a background in NLP and application development is desirable. Weekly meetings will be held to monitor progress.

Video to text using Computer Vision and Natural Language Processing

The objective of this research is to program software that implements state-of-the-art computer vision models for pose estimation and translates them into text using Natural Language Processing (NLP) in real time. Models based on deep neural networks and Transformers are considered.

Advanced programming knowledge is required, but a background in computer vision, NLP, and application development is desirable. Weekly meetings will be held to monitor progress.

Biopavements: Laboratory Characterization of Asphalt and Lignin Mixtures

Laboratory work as part of a FONDEF project aimed at developing asphalt mixtures with lignin to increase pavement durability. The work includes laboratory testing and analysis of results. The student will join a research team in Road Engineering for one semester and will be expected to participate in research meetings, as well as analyze and report results.

Experimental Validation of 3D-Printed Connectors for Timber Structures

This research opportunity focuses on the design, modeling, and 3D printing of structural connectors for timber systems. The student will work with various 3D printing materials to fabricate two types of structural joints, which will then be tested in the lab to evaluate their mechanical behavior. The project is part of a broader research initiative on teaching innovation and digital fabrication technologies for timber construction.

Implementation of CFD simulations in the OpenFOAM model to support teaching activities in Fluid Mechanics

We will work with the CFD (computational fluid dynamics) simulation model OpenFOAM (https://www.openfoam.com/) to develop numerical simulations of free surface flows as a complement to physical model experiments conducted in the Fluid Dynamics laboratory. The goal is to have a CFD tool that allows solving for the pressure and velocity fields, which can then be compared with direct measurements obtained through video and sensors for velocity and pressure in the laboratory.

Embedded Deep Learning and Federated Learning for Glucose Control

In this IPRE, the student will implement Deep Learning algorithms on microcontrollers (Jetson Nano) with the aim of developing glucose control strategies. Additionally, the use of Federated Learning algorithms will be explored to coordinate the training of multiple agents, enabling more efficient learning without the need to share sensitive data.

Modelling water transport in porous soils

Part of Carlos Faúndez Fondecyt de Iniciación Proyect: Field-scale estimation of macropore parameters for dual permeability models: An integrative approach incorporating pore-scale modeling and applied geophysics.

1. Implement a unsteady 1-D model for the transport of volumetric water content and hydraulic head in a heterogeneous soil column.

2. Analyse the effect of the non-linearity induced by the dependence of the soil hydraulic conductivity on the quantities transported.

3. Extend 1-D model to a 2-D unsteady model with azimuthal symmetry.

* The development of the model will be in Python on Julia. However, the final version of this model must translated to R. The co-supervisor Carlos Faúndez from Ing. Hidráulica y Ambiental will guide and support the translation of the code.

Implementation of Artificial Intelligence for Classrooms

This research opportunity focuses on exploring how the implementation of artificial intelligence can enhance learning in classrooms with school-aged students. The goal is to develop and evaluate AI-based solutions that support both teachers and students, fostering more inclusive, efficient, and adaptive learning environments.
We are looking for students interested in creative solutions and design. One spot is intended for a student who is ideally an IDI major.

Conservation of coastal wetlands: response of suspended solids to hydrochemical gradients

Understanding the biophysical and geochemical dynamics of suspended solids in coastal wetlands is key to assess and predict their response to human and natural stressors. However, it is still poorly known in Chile, preventing the design of adequate and timely responses to phenomena lile climate change, droughts, and soil use changes in watersheds, like urbanization.

The objectives of this workl are: (i) to sistematize and creiticallyt review the literature on suspended solids and turbidity in selected coastal wetlands; (ii) to familiarize and apply analytical tools enabling the characterization of the suspended particle dynamics and the related pollutants, either in lab or field models; (iii) to recognize the potential implications of the research findings on the proposal of adaptation/prevention/control.

Computational Modeling of Alveolar Micromechanics

Alveolar tissue supports gas exchange, the primary function of the respiratory system. To accomplish this, it cyclically undergoes stress and strain. This mechanical behavior is characterized by a non-linear relationship between stress and strain, as well as hysteresis caused by alveolar surfactant. This project seeks to develop a computational model that incorporates alveolar morphology and the non-linear behavior of the alveolar septum to understand its response under both normal and pathological conditions.

Building lung phantoms for mechanical ventilation

Phantoms are devices that replicate the anatomy and potentially the function of an organ. This project involves the design, fabrication, and validation of a lung phantom for the study of respiratory mechanics. The project combines medical image processing, 3D printing of biomodels, experimentation, and respiratory mechanics analysis to characterize lung phantoms.

Mechanical and electrical characterization of bone tissue

Bone tissue is a porous structure with a high mineral content that provides structural support for the musculoskeletal system. Its mechanical properties are difficult to determine non-invasively. An opportunity to do so is through electrical impedance measurements, a field that has been underexplored. This project seeks to study the relationship between the electrical and mechanical properties of bone tissue.

Analysis of respiratory behavior in athletes using wearable devices

Breathing plays a fundamental role in the development of exercise and physical activity. This project seeks to validate respiratory sensors for the study of athletes, and to understand which aspects of breathing are most associated with athletic performance.

Generative network of musical exercises for auditory training

In this research, we will attempt to train a generative deep learning model based on a database of music theory and auditory training exercises, using multimodal models that receive text in addition to scores. The idea is to implement a tool for personal study without the need for a teacher as a mediator.

Financial and economic analysis of urban highways

Based on the information available in the financial statements and operational reports, the aim is to determine the economic profitability of metropolitan urban highways, as well as other economic indicators (e.g.,capital cost per lane-kilometer of road), financial indicators (e.g., cost of debt), and operational indicators (e.g., cost per vehicle-kilometer).
First, the information must be processed and systematized. Second, economic and financial analyses are carried out.

An experimental platform for the control of autonomous vehicles

This research falls within the field of automatic control of IoT systems composed of autonomous vehicles that communicate with each other through wireless channels. The undergraduate research project focuses on the development and refinement of a small-scale platform for platooning autonomous vehicles using robots. Various experiments are to be implemented on this platform, including event-based control, analysis of the impact of transmission power and rate on platoon stability, and strategies to compensate for data loss, among others.

Automatic detection of audio signatures in underwater acoustic scenes

Marine ecosystems can be studied through recordings of underwater acoustic scenes using hydrophones. The objective of this project is to develop and to implement efficient methods for the detection of acoustic signatures of distinct fish species living in these ecosystems. This problem is part of an interdisciplinary collaboration with marine biologists which poses interesting computational challenges. This is due to the length of the audio recordings, the reduced number of acoustic signatures, and the low signal to noise ratio due to the diversity of the acoustic scene.

From a mathematical perspective, the detection is formulated similarly to blind deconvolution. The signal is modeled as a superposition of a few components modulated in frequency and corrupted by additive noise. To identify these components, we avoid a black-box approach, solving instead a semidefinite program. The computational challenge involves developing efficient solvers able to scale up to long audio recordings with low signal to noise ratio.

Extracting information from educational PDF documents

This research is part of the field of PDF mining for education. Specifically, it uses two databases of educational documents for all grade levels of Language and Communication in Chile. The first contains the PDF documents of all student textbooks selected by the Ministry of Education to be the official textbook for that grade level, along with their corresponding teaching guides, which suggest activities, pedagogical guidelines, and best practices for using the textbook in class. The second is a relational database created by a previous IPRE (Research Institute for Educational Research), which transformed each page of the PDFs into plain text. The challenge is to generate an automatic method to populate a table in the second database based on the multimodal information contained in the first: relating the pages of each teaching guide to the specific page of the student text to which they refer. The student text is contained as an image in the course guide in a predictable location, so computer vision techniques, interaction with the PDF source code, and/or bot creation are expected to be used to solve the task. Computational efficiency and generalization capabilities will be prioritized as the publisher and winning book change year after year, in order to continue updating the database with all its links.

Towards a spatially distributed environmental exposure assessment of pesticides in Chile by computer simulations

Various space-based products will be utilized, primarily derived from publicly available satellite data and its processed outputs. Additionally, analytical solutions will be employed to model water flow, as well as heat and solute transport. These analytical solutions will be compared with numerical models to assess their accuracy. The study will be conducted at a national scale and represents an initial stage in the environmental assessment of areas at risk of pesticide contamination in soils and groundwater. The work is entirely computational; however, students who participate can also engage in laboratory and field activities.

Technical and Social Evaluation of the New Huechuraba Electric Bus Service

This research is being conducted in conjunction with the Engineering for Chile (IPCH) program and the PUENTES UC program.

Since March 2025, the Municipality of Huechuraba has implemented an innovative electric bus system aimed at improving connectivity in areas of the municipality that currently lack formal public transportation, facilitating the mobility of residents to key services such as health centers and municipal services. In this context, Transportation Engineering students are invited to evaluate the design and initial operation of this new community service, which has two electric buses in circulation. Their work will consist of analyzing route efficiency and service frequency, as well as measuring the social impact of the initiative, identifying opportunities for improvement to optimize coverage, accessibility, and the sustainability of the system. At the end of the project, the students must submit a technical diagnosis that justifies the continuity and expansion of the service, along with concrete proposals for its improvement.

The results of this evaluation will be key to supporting applications for future externally funded projects aimed at consolidating and expanding this neighborhood service. From the outset, students will have access to updated maps of the community and technical information on the current route design and bus frequencies, allowing for contextualized work with a real impact on the community's quality of life.

Throughout the semester, we will hold weekly 30-minute meetings, along with progress presentations at a time to be agreed upon by the participants.

Seismic Signal Analysis Using Data Science

This undergraduate research project aims to analyze seismic signals using data science tools to identify patterns or relevant features in records associated with natural processes such as earthquakes, volcanic eruptions, or glacier dynamics. Depending on data availability and the outcomes of the analyses, there is a possibility of conducting complementary fieldwork to acquire new measurements or to validate the results.

Digital Health – Software Quality

The advancement of digital health has led to a proliferation of technological solutions—such as telemedicine platforms, mobile applications, electronic health record systems, and applied artificial intelligence—that do not always comply with the required standards to ensure safety, interoperability, usability, and clinical effectiveness. This situation poses risks to patients, creates challenges in integrating with existing systems, and limits the ability of healthcare providers to assess the true quality of the tools they use or acquire. At the national level, this project proposes to explore and develop a comprehensive software quality assurance model specifically focused on the field of digital health, adapted to the regulatory, technical, and clinical conditions of the Chilean and Latin American context.

The model will include evaluation criteria based on international best practices (such as ISO/IEC 25010), automated testing tools, developer guidelines, and a quality seal to identify safe and reliable solutions.
This project will not only promote the continuous improvement of the digital health innovation ecosystem but also support informed decision-making by providers, patients, and public institutions.

Developing an optogenetic printer for the precise control of biological systems

We are developing programmable platforms to precisely control the delivery of instructions to cellular systems. As a model system, we are building a programable “laser printer” capable of delivering spatially and temporally resolved light signals to activate optogenetic programs within cultured cells. This requires creating an interphase that allows dynamic control of the light input, controlling position, intensity and duration of light stimuli.

Study of the Shroud of Turin by image processing

The image on the Shroud remains a subject of study and controversy to this day. It is proposed to systematically investigate the image (positive and negative) of this relic using various image processing and analysis techniques based in artificial intelligence to compare it against similar historical images and find correlation patterns among these pictorial sources. As a result, it is hoped that a paper will be written for an international conference on this subject.

Implementation of Artificial Intelligence for Classrooms (2)

This research opportunity focuses on exploring how the implementation of artificial intelligence can enhance learning in classrooms with school-aged students. The goal is to develop and evaluate AI-based solutions that support both teachers and students, fostering more inclusive, efficient, and adaptive learning environments.
We are looking for students interested in creative solutions and design. One spot is intended for a student who is ideally an IDI major.

Bioelctrochemical systems for the treatment of industrial wastewaters

This is an applied research project with industry.
The student will work on laboratory-scale electrofermentation prototypes to stabilize wastewater from different industries, e.g., agriculture, pulp and paper.
The student will learn advanced laboratory techniques to determine the methane potential of organic industrial wastes. Additionally, they will learn how to feed and operate anaerobic and bioelectrochemical reactors, as well as how to monitor and evaluate their performance based on bioenergy production and effluent quality.
If you are interested, please contact me at rolabatut@uc.cl to schedule a meeting to discuss details and answer any questions you may have before making a decision.

Anaerobic systems for the treatment of emerging contaminants

This is an applied research project.
The student will work on the development and application of laboratory-scale anaerobic and bioelectrochemical reactors to remove emerging contaminants present in domestic and industrial wastewaters.
The student will learn advanced laboratory techniques for wastewater characterization. They will learn how to feed and operate anaerobic and bioelectrochemical reactors, as well as how to monitor and evaluate their performance based on effluent quality and the removal of specific contaminants.
If you are interested, please contact me at rolabatut@uc.cl to schedule a meeting to discuss details and answer any questions you may have before making a decision.

Computer Science Museum

This IPRE will involve conducting research on museums and how objects are displayed. Field research will then be conducted to understand the perceptions of relevant stakeholders. Finally, a proposal for the museum will be designed.

Gamification and Mixed Reality in Construction: Review and Research Opportunities

This project focuses on reviewing and analyzing the existing literature on the application of gamification and extended reality technologies (with an emphasis on mixed reality) in the training of construction workers, particularly in the field of ergonomics and the prevention of work-related musculoskeletal disorders.

The study aims to identify:
- The main trends and theoretical approaches in gamification applied to safety and ergonomics education.
- The current state of mixed reality use in construction training environments.
- The limitations and research gaps in integrating these technologies to foster ergonomic behavior change in real workplace contexts.

This study is conceived as an initial step to support future, larger-scale research aimed at developing practical immersive training platforms. At this stage, however, it will be limited to a systematic bibliographic study, with emphasis on recent academic publications.

Electricity Systems Planning and Public Policy

Research on the use and development of planning models for power systems and the design of public policies. In particular, the SWITCH model for generation and transmission expansion will be applied to the Chilean power system. The impact of integrating new technologies into power systems will be quantified, along with other elements such as increasing the use of electricity in the energy matrix and leveraging demand-side flexibility, among others.

Industrial escalation of diagnosis and prognosis methods for infectious diseases

Given the health and economic impacts of respiratory virus infections, our clinical-scientific team has developed rapid, low-cost diagnostic methods to detect RSV, MPV, and an inflammatory cytokine using immunological techniques. This project aims to expand this method by producing large batches of monoclonal antibodies developed by our team, creating a rapid test kit, and validating it through laboratory tests and clinical trials.

Bioinformatics and modeling of the gut-brain axis

Comparative genomics of probiotic bacteria, literature review, and retrieval of metabolic reconstructions from databases. Analysis of transcriptomic and metagenomic microbiome databases. Reconstruction of metabolic models integrated with transcriptomic data.
Development of metabolic reconstructions in Drosophila melanogaster for studies on neurodegeneration and the gut–brain axis.

Probabilistic Analysis of Linear Programming Hierarchies

Linear programming hierarchies offer a systematic way to strengthen the feasible region of a linear relaxation, and consequently, a potential improvement in the integrality gap. These hierarchies guarantee convergence to the value of the integer problem; however, this convergence can be very slow, or the hierarchy might even fail to certify infeasibility for simple problems. The objective of this research is to study the behavior of the integrality gap in these hierarchies for random or typical instances. These probabilistic models offer an alternative to the various worst-case results found in the literature, deepening our understanding of the optimality guarantees for this type of relaxation. During this research, we will use linear optimization tools and stochastic analysis.

Using generative AI for the de novo design of the metamorphic domain of RfaH in its autoinhibited state

The metamorphic protein RfaH is a bacterial virulence factor, which has two domains: NTD and CTD. The CTD reversible interconverts from an alpha-helical into a beta-barrel state through a metamorphic process. Although the possible folding routes between both states have been studied, it is unknown if there are other alternatives of folding topologies for the CTD in the autoinhibited state. In this regard, the use of generative AI tools for de novo protein structure design, such as RFDiffusion, allow to explore novel structures never seen in nature. The purpose of this research is, therefore, to ascertain if RFDiffusion allows to generate novel structures for the CTD of RfaH in the autoinhibited state, as well as analyzing the diversity of generated structures via clustering methods based on structural superposition metrics.

Protein extract from algae based on eutectic solvents

The objective of the project is to design an optimized and sustainable protein extraction process from Chilean marine algae using deep eutectic solvents (DES) to produce functional extracts for application in the food industry.
Algae species will be selected based on their protein content and cultivation feasibility, followed by nutritional and techno-functional characterization of the extracts, ultimately validating their incorporation into food matrices.

Teaching platform programming for power converters

Programming a teaching platform for power converters in applications such as PV, BESS, active filters, grid-tie, etc.

The project involves reprogramming and adapting a kit already implemented on a platform focused on educational activities. The main hardware (controller and converter) is available and functioning, but the control system does not need to be adapted for new applications.

The LAUNCHXL-F28379D microcontroller is programmed using the Target Support Package (an extension of PLECS Blockset and PLECS Standalone that allows generating code for microcontrollers).

Control platform: LAUNCHXL-F28379D
Three-phase inverter: BOOSTXL-3PhGaNInv (can also be used as a DC-DC inverter)
Motor: BLWS232D-24V-4000 (only if using drives)
Other possible applications: DC-DC for PV, BESS, grid-tie active filter, DAB, etc.

New materials for carbon capture and utilization

The rising concentration of CO₂ in the atmosphere mainly caused by the combustion of fossil fuels is one of the main drivers of global warming. A promising strategy to address this challenge is to capture CO₂ and convert it into valuable products such as methanol, which is widely used as both a clean fuel and an industrial feedstock. However, conventional copper‑based catalysts tend to lose activity quickly, limiting the efficiency of current processes.
This project aims to develop innovative materials capable of capturing and converting CO₂ in a single step, using so‑called dual‑functional materials. The approach focuses on copper‑based perovskites partially substituted with Zn, Ni, or Co. Through a process known as exsolution, these materials generate highly active metallic nanoparticles on their surface. The perovskites will then be combined with β‑zeolite, a porous material that both captures CO₂ and helps stabilize the active catalytic phase.
The work involves the synthesis of these hybrid materials, their characterization using advanced techniques such as X‑ray diffraction, gas adsorption, electron microscopy, and X‑ray photoelectron spectroscopy, and their testing in a fixed‑bed reactor to evaluate their ability to convert CO₂ and hydrogen into methanol. The expectation is that the perovskite-zeolite combination will yield catalysts that are more stable, active, and selective, contributing to cleaner technologies for sustainable fuel production.

Designing a Situated Mobile Augmented Reality Exhibition for Communicating Coastal Research Findings

This project aims to design and prototype a situated mobile augmented reality exhibition using smartphones, focused on communicating scientific research results related to coastal phenomena. The exhibition is conceived as a museum-like experience outdoors where users can physically move through different locations and, via their mobile devices, access visualizations, videos, and narrative content explaining wave dynamics, coastal flooding, and associated risks. The project emphasizes experience design, spatial storytelling, and communicative clarity, rather than the development of numerical simulations or scientific modeling.

The work will build upon existing scientific materials—such as simulation videos, images, diagrams, and explanatory texts—which will be reinterpreted through principles of user-centered design and public communication of science. The final outcome will be a low- to mid-fidelity functional prototype implemented in Unity for mobile devices, accompanied by a conceptual exhibition design, a narrative script, and an exploratory user evaluation. The project seeks to strengthen skills in interaction design, situated immersive experience design, and the visual translation of complex scientific knowledge.

Propulsion of Ships Powered by Batteries and Green Hydrogen

The project was awarded under the 2025 Regular Technology Rings Competition of the National Research and Development Agency (ANID). The project aims to develop a comprehensive framework for the efficient management of electric vessels that use batteries and hydrogen as energy sources. The initiative aligns with the Chilean Navy's National Green Hydrogen Strategy and its Continuous National Shipbuilding Plan, aiming to strengthen the sustainability and competitiveness of the national maritime sector. The project also has the support of the Chilean Navy and the companies Saam Towage and Dhemax, which reinforces its applied focus and its potential impact on national industry.

Main lines of research:

1. Optimal management of energy vectors under uncertainty.

2. The second focuses on electrical infrastructure and microgrids.

3. Electric propulsion and conversion systems.

IPRE is focused on line 3 but has potential for collaboration in the other lines.

Experimental Analysis and Modeling of Collective Cancer Cell Migration

This project offers the opportunity to analyze experimental data from a cancer cell migration assay and translate the findings into a computational model, extending an existing Julia-based framework for simulating collective cell migration (see: https://doi.org/10.1039/c5ib00092k; https://doi.org/10.1101/2025.08.28.672905). The student will learn to process and analyze migration data, then implement it within a simulation platform using Julia or Python—modifying and expanding the model, performing parameter sweeps, and exploring how mechanical interactions influence emergent collective behaviors.
We seek motivated and independent students committed to applying computational tools to improve our understanding of cancer progression.
Let me know if you'd like it more specific about the type of migration assay or the analysis steps involved.