Thesis offers

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.

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

This Bachelor 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.

High-performance computing (HPC) is a hot research topic that drives the interest of many researchers, institutions, and countries all around the world.  The high-performance Linpack, also known as HPL, is the quintessential bencharmk for measuring the performance of supercomputers and it is used to rank them in the famous top500.org website.  Tradditionally, this application has been run on distributed and homogeneous systems equipped with CPUs using highly optimised BLAS libraries and MPI.  With the popularisation of accelerators like GPUs to speedup computations, there has been a plethora of new HPL implementations to exploit distributed and heterogeneous systems.

The student will be asked to implement OpenMP and OmpSs-2 parallel programming models on an heterogeneous version of the HPL benchmark for heterogeneous systems.  First, he or she will perform a porting from AMD GPUs to NVIDIA GPUs by replacing the corresponding calls to specialised BLAS libraries as well as in-house low-level kernels.  Second, the student will enable task-based, shared-memory parallelism to CPU kernels, studying different types of optimisations to effectively overlap computations and communications on both CPUs and GPUs.  Third, the student will perform and exhaustive and quantitative analysis of the various factors affecting the performance of fork-join and task-based implementations.

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

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.

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.

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)

To evaluate the impact of the AI assistant, we will conduct a user study that examines both its effectiveness in supporting participants and its overall usability within the VR simulation.

Propagation graphs are a mathematical modelling method for radio propagation channels. A propagation graph is a signal flow graph in which vertices represent transmitters, receivers or scatterers, and edges models propagation conditions between vertices. Propagation graph models were initially developed for multipath propagation in scenarios with multiple scattering, such as indoor radio propagation. It has later been applied in many other scenarios. Compared with the conventional ray-tracing-based approaches, the graph-based modeling may significantly reduce the computational complexity. However, for large or dense scenarios, propagation graph modeling can still be computationally unfeasible in practice.

To address the high computational cost of propagation graph modeling, this master's thesis project aims to enhance and extend the capabilities of the Tongji University Propagation Graph Engine. The primary objective is to develop a new version of the codebase that is fully compatible with modern parallel computing environments, contemporary data formats, and heterogeneous computational architectures. The overarching goal is to maximize computational performance and scalability.

To this end, the project will integrate parallelism by leveraging PyCOMPSs (Python COMPSs), which serves as the Python binding for the COMPSs programming model and runtime. COMPSs is specifically designed to facilitate the development of parallel applications for distributed computing infrastructures, such as high-performance computing (HPC) clusters and cloud-based platforms. While providing a sequential programming interface, the COMPSs runtime dynamically identifies and exploits task-level parallelism by constructing and executing a task dependency graph during runtime. This graph reflects data dependencies among annotated methods (tasks), and the runtime system manages the efficient scheduling and execution of these tasks, including the orchestration of data transfers across computing nodes.

The PyCOMPSs ecosystem also offers a suite of tools to support application development, real-time execution monitoring, and post-execution performance analysis, thereby offering a comprehensive environment for the design and deployment of high-performance applications.

This project will be carried out in close collaboration with three research institutions: the Computer Architecture Department at the Universitat Politècnica de Catalunya (UPC, Spain), the Wave Phenomena Group at the Barcelona Supercomputing Center (BSC, Spain), and the Department of Information and Communications Engineering at Tongji University (China). As part of this collaborative framework, a short research mobility stay is planned to foster the student's engagement with international research environments and broaden their perspective in the field of high-performance computing and network simulation.

Objective: To develop a functional prototype of an eye-tracking system using HF cameras

Tasks:

1. Conduct literature review on existing techniques in eye-tracking, including open-source frameworks for computer vision

2. Reuse existing database or collect video data of eye movements from volunteers under various lighting conditions. Annotate data for training and testing (e.g., gaze points, blinks).

4. Implement computer vision algorithms to detect eye positions and movements.

5. Integrate real-time gaze tracking and visualization.

6. Validate system accuracy against benchmarks from traditional eye-tracking devices.

7. Test the system in a practical scenario (e.g., tracking reading patterns or visual attention).