Thesis offers
Check offers of other studies and specializations
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).
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.
To date, traditional Deep Learning (DL) solutions (e.g. Feed-forward Neural Networks, Convolutional Neural Networks) have had a major impact in numerous fields, such as Speak Recognition (e.g., Siri, Alexa), Autonomous driving, Computer Vision,etc. It was just recently, however, that a new DL technique called Graph Neural Network (GNN) was introduced, proving to be unprecedentedly accurate to solve problems that are formalized as graphs.
Navigation meshes are necessary to represent the walkable space of an environment so that agents can perform pathfinding and move through them. Current navigation meshes tend to flatten the environmetn to represent it as 2D polygons connected by edges (square cells, triangles or larger convex polygons). This abstraction presents problems when dealing with complex outdor geometry where the terrain may not be completely flat. With this project, we would develop a novel navigation mesh that can keep the complexity of any 3D input geometry, while still generating small graphs
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
The main objective of this TFM is to build a proof-of-concept implementation of a Network Intrusion Detection System using Graph Neural Networks and to evaluate its performance using publicly available data sets.
This is an offer for a PhD Dissertation, which can be initiated as a MS Thesis. We are working to design a domain-specific accelerator, in the context of high-performance computing, for homomorphically-encrypted deep learning inference. Previous cryptography experience is not required, since there will be an expert in the team advising on those matters. The successful candidate will be integrated into an international team of 9 members working toward this purpose on different topics.
In this project, we wish to create a summarized universal representation of packet flows within a computer network. We approach this problem as an Unsupervised Learning problem, where the summarized representation must be as small as possible while minimizing the reconstruction error. In order to build this representation, multiple approaches are to be considered. This includes using traditional techniques such as a Fourier Transformation, and using representations learned Machine Learning models, specifically Autoencoders and sequence models like 1-dimensional CNNs, RNNs
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.
In this project, we will aim at assessing the hypothesis that the same emotion recognition accuracy can be achieved when utilizing fine-grained 3D point-clouds of the human faces containing different emotions.
The main goal of this project is to assess the effects of dynamic changes in the number of co-existing VR users, as well changes in the mobility patterns of the users in the physical setups. Based on the assessed effects (and in case of a longer project such as BSc/MSc thesis), the student is envisioned to propose a method for appropriate scaling of the number of users based on their mobility patterns, sizes of deployment environments, obstacles in the deployment environment (e.g., other users).
Most EU citizens are concerned about online privacy. EPRIVO aims at building a European data-driven observatory that automatically looks for online services that do not respect our privacy rights.
Reading charts in desktop and virtual reality environments can be difficult depending on the configuration of different parameters such as the width and height of different visual marks. The project aims to explore the perceptual limits of a set of well-known visualization techniques in desktop and VR-based environments.
This is an offer for a PhD Dissertation, which can be initiated as a MS Thesis. We are working to accelerate homomorphically-encrypted deep learning inference via different efforts, in this case FPGA accelerators. Previous cryptography experience is not required, since there will be an expert in the team advising on those matters. The successful candidate will be integrated into an international team of 9 members working toward this purpose on different topics.
Programming models for HPC have to be adapted to a new paradigm where every HPC system has accelerators. In BSC we develop the OmpSs-2 shared-memory programming model, which has support for offloading tasks to GPUs and FPGAs. However, programmers must still manage memory copy operations manually, or use unified memory. The project will consist on implementing automatic memory management inside the OmpSs-2 runtime, tracking and moving data transparently from hosts to accelerators and vice-versa.
This is an offer for a PhD Dissertation, which can be initiated as a MS Thesis. We are working to accelerate homomorphically-encrypted deep learning inference via different efforts, in this case GPU accelerators. Previous cryptography experience is not required, since there will be an expert in the team advising on those matters. The successful candidate will be integrated into an international team of 9 members working toward this purpose on different topics.
The goal of this project is to develop a high-performance game engine for large-multicore and accelerated systems. The game engines are the key component that underlay all video games. It is a complex software that has to continuously process user input, execute the game logic, update the game state, produce the next frame and render it in a timely fashion. All these activities are usually expressed as a Directed Acyclic Graph (DAG) of tasks.
SPEChpc is a benchmark suite that provides a wide set of applications designed to measure the real-world performance of HPC systems. They include distributed, hybrid and accelerated (CUDA) workloads. This project consists on porting some of the applications on SPEChpc to the OmpSs-2 programming model, a data-flow task-based model which is developed in the BSC. The goal is to improve performance of SPEChpc applications leveraging OmpSs-2's advanced features
Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.
This master's thesis aims to analyze the feasibility of a remote VR system based on the use of mobile devices with cardboard glasses and low-cost interaction devices. It will start from a system based on HTC-VIVES programmed with Unity. Different portability alternatives to the new platform will be analyzed both in terms of the rendering of the models (locally or on a server) and the limitations of the interaction and connection between students and teacher. A prototype will be developed with basic interaction techniques and its usability will be analyzed.
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