Consulta ofertes d'altres estudis i especialitats
Companies and scientists working in areas such as finance or genomics are generating enormously large datasets (in the order of petabytes) commonly referred as Big Data. How to efficiently and effectively process such large amounts of data is an open research problem. Since communication is involved in Big Data processing at many levels, at the NaNoNetworking Center in Catalunya (N3Cat) we are currently investigating the potential role of wireless communications in the Big Data scenario. The main focus of the project is to evaluate the impact of applying wireless communications and networking methods to processors and data centers oriented to the management of Big Data. OBJECTIVES =========== N3Cat is looking for students wanting to work in the area of wireless communications for Big Data. To this end, the candidate will work on one of the following areas: - Traffic analysis of Big Data frameworks and applications, as well as in smaller manycore systems. - Channel characterization in Big Data environments: indoor, within the racks of a data center, within the package of CPU, within a chip. - Design of wireless communication protocols for computing systems from the processor level to the data center level.
Machine Learning (ML) has taken the world by storm and has become a fundamental pillar of engineering. As a result, the last decade has witnessed an explosive growth in the use of deep neural networks (DNNs) in pursuit of exploiting the advantages of ML in virtually every aspect of our lives: computer vision, natural language processing, medicine or economics are just a few examples. However, NOT all DNNs fit to all problems: convolutional NNs are good for computer vision, recurrent NNs are good for temporal analysis, and so on. In this context, the main focus of N3Cat and BNN-UPC is to explore the possibilities of the new and less explored variant called Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured data. This has huge implications in fields such as quantum chemistry, computer networks, or social networks among others. OBJECTIVES =========== N3Cat and BNN-UPC are looking for students wanting to work in the area of Graph Neural Networks studying their uses, processing architectures, and algorithms. To this end, the candidate will work on ONE of the following areas: - Investigating the state of the art on this area, surveying the different works done in terms of applications, processing frameworks, algorithms, benchmarks, datasets. This can be taken from a hardware or software perspective. - Helping to build a testbed formed by a cluster of GPUs that will be running pyTorch or Tensorflow. We will instrument the testbed to measure the computation workload and communication flows between GPUs. - Analyzing the communication workload of running a GNN either in the testbed or by means of architectural simulations. - Developing means of accelerating GNN processing in software (e.g., improving scheduling of the message passing) or hardware (e.g. designing a domain-specific architecture).
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 than the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computers
Millorar el nivell de realisme d'aguns dels procediments interactius d'una aplicació en un entorn de RV immersiu (casc RV) dins la qual el participant ha de poder realitzar un dels processos establerts en el protocol de donació i trasplantament d'òrgans. La idea general és incorporar tècniques avançades de Gràfics per Computador per simular de manera realista alguns d'aquests processos tenint en compte que es requereix execució en temps real.
Desarrollo de una plataforma de contabilidad en un entorno gamificado (por ejemplo, el metaverso de Facebook). La plataforma debe ser como un bot de inteligencia artificial, ayudando a un director ejecutivo no financiero a tener toda la información que le permitirá comprender el negocio, como una contabilidad gamificada manual.
Desenvolupar un sistema de monitorització de l'algorisme d'encaminament Babel en una xarxa mesh OpenWRT
This Final Degree Project (TFG) aims to improve machine learning pipelines maintainability, evolvability and replication, and ease their transition from experimentation to production. To do so, it proposes the use of object-oriented concepts to define suitable architectural elements needed to design Python machine learning pipelines in Jupiter Notebook. To demonstrate the feasibility of the proposal, an example in the domain of tweet sentiment analysis is presented.
El objetivo principal del TFG es la implementación de un sistema de directorio (estructura de datos para saber donde estan los datos en un modelo paralelo basado en tareas muy parecidas a las de OpenMP) en un sistema con soporte para diferentes dispositivos. El trabajo se realizará con el soporte de una beca de cooperación a media jornada en el BSC e integrado en el grupo de trabajo de modelos de programación paralela del BSC y de la UPC.
En este proyecto el alumno evaluará el rendimiento de aceleradores específicos para HPC. Para medir este rendimiento una posibilidad es realizar el porting de partes de alguna aplicación bioinformática con tal de acelerarlas. El/La alumno/a realizará el TFG integrado en un grupo de trabajo formado por otros estudiantes del BSC y de la UPC.
We have developed LoRaMesher, an on-going implementation for doing mesh networking with LoRa nodes. https://github.com/LoRaMesher/LoRaMesher The TFM will develop LoRaMesher further on a specific topic, such as embedded systems, network level, machine learning or application level, according to the interest.
The objective of this project is to explore federated machine learning in TinyML.
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