Ofertas de proyectos

The future of communication is taking us beyond traditional terrestrial networks. In the next decade, 6G networks will connect more than just smartphones-they will support flying cars and underwater robots using such high-tech enablers as lasers, satellites, drones, and high-altitude platforms (HAPs, flying at 20-40 km). To make this vision a reality, we need to tackle challenges like ensuring seamless connectivity for high-speed aerial vehicles, enabling underwater data transmission, and predicting large-scale network performance in urban environments. By working on these problems, you will be at the forefront of working on groundbreaking challenges in the rapidly evolving field of 6G and Non-Terrestrial Networks.  

The student will work on one of the following research objectives (or on a sensible combination of them):

- Performance modeling and optimization of communication links with satellites (such as starlink) and high-altitude platforms

- Development of advanced AI-driven beamsteering solutions to improve data transmission in underwater laser communication

- Design of an underwater-to-satellite (or HAP) communication system

- 6G-enabled 3D weather sensing and climate monitoring

- Usage of open big real-world data (e.g., OpenStreetMaps) to predict network coverage and performance in large urban areas, helping to optimize infrastructure deployment

- Design of 6G handover strategies for reliable connections with highly mobile (up to 300 kmh) flying cars/taxis

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.

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.

This approach is essential for studying advanced network solutions, including scenarios where machine learning and AI techniques are applied for radio resource management, optimization of data rates, and network reliability.

This Final Degree Thesis will be co-supervised by UPC Co-advisor: Cristhian J. Rodrigues Dos Santos (now in Mobility at NII-Tokyo)

A financing for the student is available depending on the level of the student, to be agreed with the company, NOKIA, collaborating on the project.

To develop a virtual 5G core and RAN architecture that accurately replicates the structure and operational conditions of a real research network, providing a flexible and robust platform for network experimentation, optimization tests, and evaluation of emerging 5G/6G technologies in a safe, non-disruptive environment.

Thematic Areas that you will learn:
- 5G and Beyond Network Architecture

-Digital Twins in Telecommunications

- Virtualized Core and RAN Deployment

- Radio Resource Management (RRM)

- Artificial Intelligence and Machine Learning in Telecommunications

- Network Simulation and Emulation for Research and Development

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).

State-of-the-art models such as LLMs are too large to fit in a single compute node (GPU, NPU, CPU), both for training and inference on a device (e.g., phone, laptop, tablet) or in larger-scale data centers. There is a need to develop optimization techniques to split and place these models onto a distributed set of compute nodes so that the overall system performance is maximized. The research will be focused on optimizing the placement of AI models onto distributed systems considering training time, energy consumption, and computational resources. 

In this thesis, the student will model and simulate distributed AI workloads using both mathematical frameworks and simulators. This encompasses the modeling of network, compute, and memory components within a distributed architecture. Developing new modules to enhance the modeling process. Evaluating and optimizing various parallelization techniques to improve overall system performance.

The Universitat Politècnica de Catalunya · BarcelonaTech offers Master thesis fellowships in the field of LLM training. The research will be supported by Qualcomm and will be carried out in an environment with a strong interaction with leading experts in the field, with opportunities for doing internships in the company.

More information here: https://www.cs.upc.edu/~jordicf/priv/eda/llm_qc.html

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. A large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10) is available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019¿2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data.  MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne, and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. Data has to be map-matched to the OpenStreetMap (OSM) road graph. City datasets can be compared with other publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). A comparison of the differences across some of the datasets in spatio-temporal coverage and variations in the reported traffic will be addressed in this project. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings. A pipeline is already available to derive the dataset from the Traffic4cast data published by HERE. HERE is a technologal company providing a platform for the visualization and analysis of location data. To ensure data privacy, the Traffic4cast dataset was published as rasterized and aggregated cell-based data, nevertheless providing a high spatial and temporal resolution.

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.

Vision Transformers (ViT) have become the state-of-the-art technique underlying many computer vision tasks. The ability to deploy them on edge devices spreads the computing load and also enables applications that require real-time performance, enhanced privacy, or offline operation. However, the computational complexity and memory demands of ViTs often make them impractical for edge applications.

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.

This Master Thesis will take place at the Departament d'Arquitectura de Computadors (DAC) in collaboration with the STAR group at the Barcelona Supercomputing Center (BSC).

The System Tools and Advanced Runtimes (STAR) group at BSC focuses on research crossing multiple software layers, from OS, runtimes, and low-level APIs, to programming models, tools, and applications. We run traditional HPC, AI, and Data Analytics applications on massively parallel HPC and Cloud platforms.

Machine learning (ML) has experienced a sustainable growth during the last decade and more lately with the emergence of LLMs driven almost exclusively by industry corporations. The size of such models is currently one of the major limitations to deploy them on resource-constrained environments. CPU manufacturers have begun introducing new hardware extensions such as VNNI and AMX in order to accelerate mixed-precision calculations during ML training and inference of low-bit precision LLMs.

The student will be tasked to quantise an already existing pretraining code and measure the gains in performance as well as the potential issues like accuracy losses. Next, he or she will optimize some of the most time-consuming kernels by relying upon optimised third-party libraries. Two variants of shared-memory parallelism will be explored in conjunction with distributed-memory parallelism to benchmark the model and identify potential bottlenecks. Finally, several LLMs will be analysed and compared.

The student will have the support from researchers within the STAR group, which will provide them guidance throughout the duration of the thesis. The GTP models will be evaluated using the computational resources of our group.

Advanced Persistent Threats (APTs) are becoming one of the most significant cybersecurity challenges nowadays. These attacks are characterized by being developed in a stealthy manner and over long periods of time (e.g., months or years), causing financial losses, data breaches, and operational disruptions. Famous examples of APTs are the SolarWinds cyber-attack and NotPetya, affecting multiple government agencies and global enterprises like Maersk. To defend from such threats, methods based on audit logs have been widely developed to detect suspicious behaviors in the system. Specifically, cybersecurity experts switched their attention to data provenance to develop tools and methods to combat APTs. Data provenance consists of parsing system logs and building provenance graphs that describe the entire system execution.

The objective of this thesis is to develop novel ML-based techniques for detecting APTs. During this project, the student will work with industry experts from Telefónica towards the design and implementation of a new set of tools for anomaly detection in audit logs. The student will gain hands-on experience in working with real-world datasets and will acquire technical skills to develop state-of-the-art anomaly detection techniques. In addition, the student will gain experience in graph-based ML, sequence modeling, and advanced embedding techniques.

Objectives:

· Design and develop novel pipelines for processing system log information.
· Research, apply and develop state-of-the-art methods in anomaly detection and cyber threat intelligence.
· Develop techniques for provenance-based intrusion detection systems to trace attack patterns.
· Contribute to research publications and technical documentation to communicate the results to international venues or conferences.

The Ride-Sharing Problem (RSP) seeks to efficiently match travelers with similar itineraries and schedules on short notice, yielding substantial social and environmental benefits by reducing the number of cars used for personal travel and optimizing the use of available seat capacity in urban areas.

The static version of RSP is inadequate for real-world applications, as it does not account for the inevitable delays caused by traffic, cancellations, or new services. Therefore, a dynamic version is necessary to accommodate these circumstances. This TFM proposes the development of a Hybrid Metaheuristic to address the Dynamic RSP, combining Metaheuristic optimization with Agent-based simulation. This optimization method must include the following features:

• Adaptation to traffic conditions
• Integration of new services en route
• Management of route cancellations

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 scaling and adaptation of the existing quantum computing architectures to specific quantum machine learning algorithms (QAOA, VQE, others)

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.