Ofertes de projectes

L'objectiu del projecte és afegir noves mètafores tant de manipulació com d'interació a la plataforma de visualització de models volumètrics en un entorn de realitat virtual.

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).

La principal problemática en Realidad Virtual es la imposibilidad de estudiar entornos de alta densidad de población, debido a la falta de realismo que existe entre la visualización y las sensaciones táctiles (empujones, presión). Es por ello que nos proponemos estudiar el uso del Joystick háptico para dirigir el movimiento de un avatar en un entorno con una gran densidad de agentes. Pretendemos evaluar si el uso de estos dispositivos permite aumentar el realismo y la inmersión, para avanzar en este tipo de estudios con escenarios que presenten una gran densidad de objetos dinámicos.

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.

Quantum computing aims to provide significant speedup to many problems by taking advantage of quantum mechanical properties such as superposition and entanglement. Important applications such as Shor's integer factoring algorithm and Grover's unordered database search algorithm provide potentially exponential and quadratic speedups, respectively.

The Barcelona Neural Networking Center (BNN-UPC) is offering two positions to develop the Master Thesis in the field of Graph Neural Networks (GNN) applied to computer networking. This TFM will be fully funded and will be carried in the context of a large industrial project with a major multinational technology company.

People working in companies spend around 28% of their time reading/answering email (see https://hbr.org/2019/01/how-to-spend-way-less-time-on-email-every-day). In this project we will propose a system based on Natural Language Processing (NLP) and Machine Learning to look for opportunities of automation arising from recurrent email patterns found at the text from emails.

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.

Deployment of distributed systems requires an Orchestrator to create pipelines, where a set of heterogeneous functions are connected. The project consists of implementing an Orchestrator and demonstrating the deployment and reconfiguration of pipelines on a 5G network scenario. The Orchestrator will run an optimization algorithm to assign the different functions into datacenters and then coordinate with the Virtual Infrastructure Orchestrator(VIO) and the Software Defined Networking(SDN) controller to create such functions.

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.

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.

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.

The main goal of this project is to develop a network monitoring system that can be used by network operators to detect bitcoin miners (or miners from other blockchain technologies) in their network. The system will rely only on network measurements obtained by standard network measurement tools and estimate interesting characteristics of detected miners, such as power consumption. How to apply: Please send an email to with your CV and academic file (pdf can be generated from the Raco).

Traditionally, Software Defined Networks (SDN) have been applied for configuring networks for which such configuration was intended to last for hours or even, days. However, the high dynamicity foreseen for 5G scenarios requires adapting SDN to be able to dynamically configure the network with much finer granularity (e.g. in the order of seconds) to adapt to high traffic variations. In this project, we will implement some components for such architecture and test them in an experimental platform.

People working in companies spend around 28% of their time reading/answering email (see https://hbr.org/2019/01/how-to-spend-way-less-time-on-email-every-day). In this project we will propose a system based on Machine Learning applied on the email actions performed to look for opportunities of automation arising from recurrent email patterns found at the text from emails.

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.

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.

The identification of the applications behind the network traffic (i.e. traffic classification) is crucial for ISPs and network operators to better manage and control their networks. However, the increasing use of encryption and web-based applications makes this identification very challenging. This problem is exacerbated with the widespread deployment of content distribution networks (e.g. Akamai) and cloud-based services (e.g. Amazon AWS). The goal of this project is to develop a traffic monitoring tool to accurately identify web services from HTTPS traffic, including Google, YouTube, Facebook, Twitter among others. The tool will combine the information from IP addresses and DNS, with novel classification methods inspired on the Google PageRank algorithm to identify encrypted traffic, even if served from Akamai, AWS or Google infrastructures. This project will be carried out in collaboration with the tech-based company Talaia Networks (https://www.talaia.io), which develops cloud-based network monitoring solutions.