Ofertes de projectes

The student will develop a modular architecture that supports synchronous queries for real-time data retrieval, as well as a configurable polling mechanism for historical data collection at customizable intervals. Additionally, the project will require implementing dynamic methods to easily incorporate new metrics into the system, tailored to their specific data type and source. The service aims to provide actionable insights for app developers, marketers, and stakeholders by consolidating diverse sources of information into a unified platform.

Objectives:

1. Design a modular and extensible architecture for aggregating and analyzing app-related metrics from diverse sources.
2. Implement real-time data retrieval and historical data collection mechanisms.
3. Provide an intuitive interface with interactive visualizations for actionable insights.

Expected Outcome:
A scalable, user-friendly tool that consolidates app-related metrics into a unified platform, enabling real-time analysis and historical trend monitoring to support decision-making for app development and marketing.

La inteligencia artificial puede resultar una herramienta valiosa para mejorar la eficiencia, la precisión y la calidad de los procesos en la planta de producción de alimentos y para mejorar la valoración de la calidad del producto final. Se propone la introducción de tecnología NIR (Near Infrared Reflectance) y de otros sensores como sistema de control, monitoreo y predicción de la calidad de las elaboraciones en planta de producción de alimentos de forma automatizada. Por tanto, las herramientas de IA permitirán la mejora de la calidad de las elaboraciones, en un desarrollo en 3 fases dentro de la cadena:

(1) Modelado de materias primas en planta con el NIR (producto en fresco).

(2) Calidad asegurada en la elaboración de cremas, mediante monitoreo de procesos y corrección de parámetros de forma automatizada.

(3) Valoración de la calidad de producto final (nutricional, químico y sensorial) que percibe el consumidor.

Este proyecto se realiza en colaboración con IRTA Lleida y Ametller Origen.

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.

A common trait of current computing systems is that their internal data communication has become a fundamental bottleneck. Traditional interconnects are insufficient and threatening to halt progress unless fast and versatile communication alternatives are developed.

In the NaNoNetworking Center in Catalunya (N3Cat, www.n3cat.upc.edu), we are investigating the use of ultra-short range wireless communications an alternative to current interconnects. In our EU project EWiC, we aim to emulate chip-to-chip wireless communications within a computer. For that, we will emulate the behavior of a multi-chip CPU with FPGAs, and connect them wirelessly using Software-Defined Radios (SDR). 

N3Cat is looking for students wanting to acquire hands-on experience in the programming and interconnection of FPGAs, and to participate in a cutting-edge project to interconnect these FPGAs wirelessly. In more detail, the objectives of the thesis are:

- To help establishing the emulator and be able to simulate its behavior.
- To work in the implementation of adequate communication protocols for the integration of wireless interconnects in the emulated multi-FPGA system.
- To assess the performance of the system via measurement campaigns.

As a result we expect a refinement of the algorthm, if needed, and some criteria to decide the representation of sets to be used according to some parameters if the bipartite network.

Advisor/s of the work: the work will be advised by Dr. Edelmira Pasarella (edelmira@cs.upc.edu) from Dept. of Computer Science and by Dr. Maria-Esther Vidal (maria.vidal@tib.eu), Head of the Scientific Data Management group at the Leibniz Information Centre for Science and Technology-TIB, and L3S Centre at the Leibniz University of Hannover, Germany. Full Professor (retired) from University Simón Bolívar, Venezuela.

Desenvolupament d'una aplicació basada en IA que ajudi a eficientar els processos de planificació i programació de l'activitat quirúrgica i intervencionisme quirúrgic. La solució haurà de facilitar una proposta de planificació/distribució temporal eficient de les intervencions quirúrgiques, en funció de l'anàlisi de paràmetres de l'històric i la demanda en la planificació. Alguns dels paràmetres que hauran de considerar-se:

Recursos assignats, humans i físics.

• Equipament especialitzat.

• Flux de densitat d'ocupació de dispositius successius (URPA, sales, etc.).

• Característiques rellevants del pacient.

• Tipo de cirurgia, complexitat del procediment i risc de complicacions.

El model haurà de prioritzar els pacients avaluant la seva urgència i prioritat basant-se en:

• Condició mèdica del pacient (per exemple: malignitat, càncer, etc.).

• Variables clíniques bàsiques(per exemple IMC, ASA)

• Temps d'espera, temps en garantia.

Per poder avaluar si es compleix l'objectiu de planificació eficient/optimització de la planificació es considerarà sobre l'output de decisió de la solució el següent:

• Minimitza temps morts entre cirurgies.

• L'equilibri entre urgències i cirurgies programades.

• L'optimització de l'ús de recursos escassos.

• La reducció de temps d'espera per a pacients.

• Millora en l'ús de recursos hospitalaris.

• Disminució de cancel·lacions i reprogramacions.

• Percentatge d'acceptació de les planificacions suggerides al personal mèdic (o sota volum de canvis en relació amb les propostes facilitades).

Un cop validada la proposta de planificació i programació quirúrgica, l'eina hauria de generar automàticament les peticions clíniques quirúrgiques a SAP.

Així mateix, per temes de confidencialitat i seguretat de la informació i en compliment de la normativa vigent s'haurà de tenir en compte:

• Possibilitat d'integració securitzada amb sistemes i/o BBDD existents de l'hospital.

• Assegurar mecanismes de transparència en el procés de presa de decisions/proposades del sistema.

• Assegurar la transparència en el refinament iteratiu basat en retroalimentació d'usuaris.

• Garantir la privacitat i seguretat de les dades de pacients en tot moment.

• Garantir les consideracions ètiques i de seguretat i la no presència de biaix en les seves decisions per motius de gènere, ètnia o edat.

Key technical objectives include enforcing best programming practices, such as pointer consistency, to enhance memory safety and efficiency. The project also emphasizes the development of a structured and extensible documentation framework and the integration of robust test coverage mechanisms to ensure comprehensive validation and stability of the code.

Additionally, the project involves close collaboration with ongoing development cycles, requiring regular software engineering feedback, identification and reporting of computational bugs, and submission of targeted patches for bug resolution. Under the guidance of Dr. Stefano Zampini (KAUST) and Dr. Octavio Castillo-Reyes (UPC/BSC), this project offers a unique opportunity to apply rigorous programming methodologies to a high-impact scientific computing application, contributing to advancements in parallel computing, memory management, and best practices in HPC-based software development.

Recent advancements in nanotechnology have enabled the development of means for sensing and wireless communications with unprecedented miniaturization and capabilities, to the point that they can be introduced into the gastrointestinal tract inside a pill or into the bloodstream in the form of passively flowing nanomachines. 

This opens the door to the idea of intra-body communication networks, this is, a swarm of nanosensors inside the human body that use communications to coordinate their actions to sense and localize specific events (lack of oxygen, biomarkers, etc). This can lead to the development of applications such as continuous monitoring of diabetes, detection and localization of cancer micro-tumors, or early detection of blood clots. These possibilities are currently investigated by our team at the N3Cat (www.n3cat.upc.edu).

In this context, We are looking for excellent and self-motivated individuals who are eager to develop ideas that will have a positive societal or socio-economic impact. In particular, the students will work on ONE of the following areas:

• Developing means of zero-power communications between nanosensors inside the human body.
• Designing protocols for communications from nanomachines inside the human body and implants or antennas in the skin surface.
• Studying mobility inside the gastrointestinal tract and human bloodstream towards creating mobility models for accurate simulation of intra-body communications.
• Dimensioning the system (inferring the required nanomachine density) to achieve a particular sensing goal/application.
• Developing AI schemes for the localization of events inside of the human body.

Seeing that not all neural networks fit to all problems, and that relational data is present in a wide variety of aspects of our daily life, the main focus of this thesis in N3Cat (www.n3cat.upc.edu) and BNN-UPC (www.bnn.upc.edu) is to explore the possibilities of the Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured relational data. We are looking for students willing to study the uses, architectures, and algorithms of GNNs. To this end, the candidate will work on ONE of the following areas:

• Uses: Applying GNNs in emerging application areas, including but not limited to (1) electroencephalogram (EEG) analysis for Alzheimer's disease detection, epilepsy classification, motor imagery; (2) acceleration of the compilation of quantum computing algorithms
• Architectures: Investigating ways to accelerate the processing of GNNs in multiple computing platforms (CPU, GPU, accelerators).
• Algorithms: Developing meta-learning data-driven models to estimate the accuracy of a GNN for a given graph, without training, through synthetic graph generation and correlation analysis.

GIS technology gives a wide-ranging support to environmental modeling, especially to groundwater modeling. Numerous GIS applications have been developed to define the hydrogeological conceptual model as well as the numerical model. However, there is a scarcity of tools to implement the conceptual model in numerical modeling platforms. This transition needs of specific methodologies to adapt the geometries and alphanumerical data from the conceptual model to the numerical model to get optimal numerical results. Although most necessities can be satisfied with inherent GIS tools, there are particular steps in the implementation of hydrogeological conceptual model into the numerical modeling software that have not been automatized and put together yet. They are mostly related to finite element mesh generation.

To address this gap, the project aims to enhance and expand a set of computational tools called ArcArAz, which focus on the preprocessing of geometric configurations and parameterization for groundwater numerical models. These tools can also be applied in other environmental sciences. The new version of ArcArAz is expected to be compatible with modern GIS systems, data formats, and computational architectures, optimizing performance to its fullest potential.

This project will be conducted in collaboration with the Universitat Politècnica de Catalunya (UPC), the Institute of Environmental Assessment and Water Research (IDAEA), and the Barcelona Supercomputing Center (BSC).

La integración de RINA con dispositivos basados en el estándar IEEE 802.15.4 permitirá una gestión más eficiente y segura de las redes de sensores, optimizando el rendimiento y la escalabilidad en aplicaciones de Internet de las Cosas. Este proyecto abordará la adaptación de tramas, gestión de recursos, seguridad y calidad de servicio, proporcionando una solución innovadora que mejorará la interoperabilidad y la funcionalidad de los sistemas IoT. La investigación incluirá el diseño, implementación, pruebas y optimización de la shim, con el objetivo de crear una plataforma robusta que potencie las capacidades de RINA en la gestión de redes de sensores y dispositivos inteligentes. La shim 802.15.4 se integrará con la arquitectura RINAsense, el cual es una librería para el rápido prototipado de sensores RINA.

Computing systems are ubiquitous in our daily life and have transformed the way we learn, work, or communicate with each other, to the point that progress is intimately tied to the improvements brought by new generations of the processors that lie at the heart of these systems. 

A common trait of current computing systems is that their internal data communication has become a fundamental bottleneck. Traditional interconnects are insufficient and threatening to halt progress unless fast and versatile communication alternatives are developed.

In the NaNoNetworking Center in Catalunya (N3Cat, www.n3cat.upc.edu), we envisage a revolution in computer architecture enabled by the use of wireless communications (and other unconventional interconnect technologies) inside computer chips, both classical and quantum. Of course, this vision comes with its own set of challenges since we need to figure out the best way to architect these systems with the new capabilities of the interconnects. In this direction, the candidate will work on one of the following areas:

• Analysis of the communications workload in nowadays computer chips and development of network-on-chip architectures to serve such traffic optimally.
• Development of chiplet-based architectures in the classical domain in cache-coherent based systems, delving into the cache coherence protocol and the interface with the wireless interconnect.
• Development of a supercomputer-in-a-package, a chiplet-based architecture based on message passing, delving into how to speed up the MPI primitives with wireless communications.
• Study of scalable quantum computing systems enabled by wireless classical communication across the chips of the quantum computer and inside a cryogenic refrigerator.

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, a quantum computer faces many challenges relative to the movement of qubits which is completely different from the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computers enabled by quantum communications, following the current European projects at N3Cat (www.n3cat.upc.edu) on scalable quantum computing.

The interested candidate will work in a group of several PhD students and in collaboration with Universitat Politècnica de València, working in ONE of the following areas:

• Designing classical (bit) and quantum (qubit) communication protocols and network architectures for communications inside a quantum computer.
• Assisting in the modeling of quantum short-range communications within our simulation framework, which is based on NetSquid.
• Developing qubit mapping, partitioning, and routing for scalable quantum computers.
• Study the use of AI to model quantum computers or develop qubit mapping and routing schemes.
• Study the potential adaptation of the existing quantum computing architectures to specific quantum machine learning algorithms (QAOA, VQE, others)