Ofertas de proyectos

Virtual reality is becoming a tool for helping patients with several illnesses (strokes, frozen shoulder...) to engage in the rehabilitation exercises.

The purpose of this project is the creation of a serious game that helps patients to engage in rehabilitation exercises for the frozen shoulder.

If you are interested, do not hesitate to ask for more information. But please, before doing so, check my "Prospective students" tab on my webpage.

Gradient descend based RBM learning [1] is a computationally expensive procedure that requires the computation of a probability normalization constant (called the Partition function) that involves the computation of an exponentially large number of terms. In order to bypass that, different approximations exist, the most celebrated one being the Annealed Importance Sampling algorithm [2], that can be parallelized in one of its dimensions.

The goal of this project ir to implement the Annealed Importance Sampling for Restricted Boltzmann Machine (RBM) using python/CUDA, and compare the performance against a standard CPU implementation.

[1] "Training restricted Boltzmann machines: An introduction",
Asja Fischer and Christian Igel, Pattern Recognition 47, 25-39 (2014).

[2] "Annealed Importance Sampling", Radford M. Neal, Statistics and Computing 11, 125-139 (2001).

Public datasets on the usage of flagship high-performance computing (HPC) platforms are fundamental to test new scheduling algorithms. But, because these platforms are critical national infrastructure, this data cannot be published. In this project, we will apply the differential privacy anonymization technique to HPC logs. This is a cutting-edge technique of growing interest within the research AI community and currently used by the US Census and the Swiss
Census. Differential privacy adds noise to the dataset in a controlled manner in order to ensure that the output statistics are contained within bounds. In this project, the student will learn and apply differential privacy to public HPC datasets and test how the results are altered by the added noise. The outcome of this research will provide a solution for HPC admins to publish their data and the methodology for the community to treat them.

To evaluate the impact of the AI assistant, we will conduct a user study that examines both its effectiveness in supporting participants and its overall usability within the VR simulation.

High-Performance Computing (HPC) plays a crucial role in scientific modeling applications that require solving large-scale numerical problems efficiently. PETGEM is an open-source 3D electromagnetic modeling tool developed to solve Maxwell's equations using a high-order vector finite element method. Originally implemented in Python, PETGEM has demonstrated significant flexibility and scalability on HPC systems. However, due to the increasing demand for performance and adaptability to emerging pre-exascale architectures, a complete migration of the codebase to the C programming language is underway.

This project focuses on the refactoring of PETGEM's magnetotelluric (MT) modeling kernel, rewriting it from Python to C. The objective is to improve computational performance, reduce memory footprint, and ensure compatibility with low-level parallelization frameworks and memory management strategies suitable for modern HPC environments. A strong emphasis will be placed on code verification, ensuring the numerical correctness of the C implementation through comparisons with the original Python version using realistic geophysical models.

The project addresses core areas of Informatics Engineering, including software refactoring, numerical computing, parallel programming, and scientific software engineering. The student will gain hands-on experience in HPC programming practices, C language optimization techniques, and validation workflows for scientific simulations.

To overcome this limitation, the project will implement a Python-based framework capable of structuring, interpolating, and rasterizing Curie depth data into GIS-compatible thematic maps. The workflow will incorporate statistical validation procedures to ensure robust and reliable spectral estimates. In addition, the entire system will be containerized using Docker and maintained in open-access repositories, thereby ensuring reproducibility, transparency, and alignment with open science principles.

The proposed framework will provide a cost-effective and scientifically validated approach for regional-scale geothermal assessment. By enabling the systematic generation of Curie depth and geothermal gradient maps, it will enhance the characterization of geothermal resources, supply valuable inputs for heat flow modeling, and contribute to advancing lithospheric studies. Beyond geothermal exploration, this reproducible methodology will promote best practices in computational geoscience, offering a reference framework that can be extended to future research initiatives and foster international collaborations

This project focuses on improving the performance and usability of an existing Python code used to simulate wave propagation in two dimensions (2D) using three numerical methods: the pseudospectral method, second-order finite differences, and third-order finite differences.

The code is part of the course "Bojos per la Ciència: Simulate the Earth", offered at the Barcelona Supercomputing Center (BSC). This course is included in the Bojos per la Ciència program, coordinated by Fundació Catalunya La Pedrera, which encourages scientific and technological vocations among high school students by providing theoretical and practical sessions led by researchers at leading Catalan research institutions.

Given that the target audience is composed of students around 18 years old, many of whom have limited prior knowledge in computer science, the project aims to enhance the code to facilitate its use during course sessions. This includes optimizing performance where possible, improving code readability and structure, and adding user-friendly features that aid comprehension and interaction during the teaching process.

The project also includes validating the numerical correctness of the improvements, studying key numerical properties such as stability and dispersion, and ensuring the code runs efficiently on high-performance computing platforms like MareNostrum.

This work will provide valuable experience in scientific computing, software optimization, and educational software development within HPC contexts.

A partir d'un model matemàtic d'aproximació amb funcions elementals, es vol descriure els quatre tipus clàssics de corbes de disparitat de fixació (FDC). Posteriorment, caldrà fer la corresponent implementació en diferents dispositius per tal d'analitzar aquestes FDC d'una forma objectiva. D'aquesta manera es vol millorar el diagnòstic i el maneig dels trastorns de la visió binocular.

Quantum algorithms are rapidly advancing as a bridge between fundamental mathematics, physics, and emerging quantum technologies. This project portfolio explores three complementary directions at the intersection of quantum linear algebra, circuit design, and graph-based quantum dynamics. By extending classical numerical methods into the quantum domain, developing efficient approaches for generating randomness and expressibility in parameterized circuits, and investigating generalized quantum walks on complex graph structures, the projects aim to deepen our theoretical understanding while advancing near-term applications in computation, communication, and physical modeling. Together, they highlight the diversity of strategies required to harness quantum computing for real-world problems, blending rigorous mathematics with practical implementation challenges.

The candidate will work on one of the following objectives/projects:

1. Quantum Linear Algebra: Quantum QZ Algorithm - Develop a quantum-inspired extension of the QZ algorithm to solve generalized eigenvalue problems, using manifold-based optimization and quantum QR decomposition. Benchmark the approach against classical methods with applications in quantum chemistry, control theory, and machine learning.

2. Quantum Circuit Design: Haar-Random Unitaries and Low-Depth PQCs - Design new parameterizations of the unitary group to enable efficient Haar-random unitary generation. Map these to hardware-friendly circuits and create shallow parameterized quantum circuits that balance expressibility with practical constraints of near-term quantum devices.

3. Quantum Walks: Dynamics on Complex Graphs - Investigate generalized quantum walks on directed, mixed, and embedded graphs, analyzing key dynamical properties and using them for algorithmic applications such as quantum search, Hamiltonian simulation, and secure communication. Explore circuit-based and optical models for near-term realizations.