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Through this project, a variation of Laplacian-based Graph Neural Networks will be developed. The main purpose is to perform temporal predictions in a large-scale Knowledge Graphs with highly heterogeneous data. In addition, the model will also be implemented for GPU high training performance and tested in a real application case for vulnerability prediction at the city of Barcelona.

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, quantum computing faces many challenges relative to the scaling of the algorithms and of the computers that run them. This thesis delves into these challenges and proposes solutions to create scalable quantum computing systems.

S'ofereix una beca d'iniciació a la recerca de 20 hores/setmana amb un salari aprox. de 600 Euros/mes per realitzar el TFM en el marc del projecte Eprivo.eu.

We offer TFM positions at the Barcelona Supercomputing Center (BSC) in a join collaboration between BSC and UPC, in the context of the EU project SAFEXPLAIN (https://safexplain.eu/). The goal of the TFM is assessing existing solutions to measure whether training and validation data used for DL models provide sufficient coverage against relevant or expert-designed features (e.g., object characteristics, weather conditions) in the context of autonomous driving.

This thesis aims to explore the possibilities of the new and less studied variant of neural networks called Graph Neural Networks (GNNs). While convolutional networks are good for computer vision or recurrent networks are good for temporal analysis, GNNs are able to learn and model graph-structured relational data, with huge implications in fields such as quantum chemistry, computer networks, or social networks among others.

Recent advancements in nanotechnology have enabled the concept of the "Human Intranet", where devices inside and on our body can sense and communicate, opening the door to multiple exciting applications in the healthcare domain. This thesis aims to delve into the computing, communication, and localization aspects of the "Human Intranet" and how to practically realize them in the next decade.

The syntactic structure of a sentence can be represented as a tree where vertices are words and arcs indicate syntactic dependencies between words. Syntactic dependency parsing is the branch of computational linguistic concerned with the extraction of syntactic dependency structures from raw text. This research proposal is focused on unsupervised syntactic dependency parsing, i.e. methods to extract syntactic dependency structures from unlabelled data. This projects consists of implementing simple unsupervised parsers and evaluating them on human languages and other species

Reliable and accurate generation of synthetic tabular health data will be validated using statistical validation. Moreover, privacy concerns will be considered by employing techniques as differential privacy. Methods for generation will be mainly based on Generative Adversarial Networks and Diffusion Models.

UPC and Nestlé are offering a new position to develop the TFM in the field of Machine Learning and Cybersecurity. This TFM will be fully funded (internship) and carried out in collaboration with the Global Security Operations Center of Nestlé and UPC.

S'ofereix una beca d'iniciació a la recerca de 20 hores/setmana amb un salari aprox. de 600 Euros/mes per realitzar el TFM en el marc del projecte GRAPHSEC.

This project will be done in collaboration with Telefonica Research. Telefonica Research is a diverse, multidisciplinary and international group of scientists who dare to push the frontiers of knowledge and prepare for the upcoming challenges on communications and the Internet.

Consulta ofertes d'altres estudis i especialitats