Ofertas de proyectos

Herein a preliminar overview of the planning of the TFM project containing the main tasks:

Task 1 (Starting point)

To get familiarized with stream processing, the dynamic pipeline approach and the windowing mechanisms proposed in the literature, graph databases, property graphs 

Task 2 (Specification of the continuous query engine)

To define the query engine abstraction and to define the architecture of the continuous query engine according to the dynamic pipeline approach

Task 3 (Dataset creation and experiments design)

To create a synthetic bank dataset representative enough for carry out an experimental assessment of the designed and implemented Continuous Query Engine.

Task 4 (Proof of concept I: A continuous query engine with a single window support) 

To implement a (preliminary) query engine for evaluating continuous queries for detecting anomalous usages of bank cards by  considering only one time-based sliding window.

To assess the preliminary continuous query engine running experiments and using the diefficiency metrics to measure the progressive delivery of results. To report and analyze the results

Task 5 (Proof of concept II: A continuous query engine with (multiple) window policy support)

To extend the preliminary continuous query engine by implementing a suitable (multiple) window policy and assess the upgraded version evaluate it in the same way as the preliminary continuous query engine was evaluated.

Task 6 (Writing) This task must be carried out together with all the other tasks during  the project development.

TFM documentation must be maintained continuously updated and refined during the development of the project.

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc
Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peñaor apply directly:

https://webapps.bsc.es/recruitment/job/2444

The integration of RINA with devices based on the IEEE 802.15.4 standard will allow more efficient and secure management of sensor networks, optimizing performance and scalability in Internet of Things applications. This project will address frame adaptation, resource management, security and quality of service, providing an innovative solution that will improve the interoperability and functionality of IoT systems. The research will include the design, implementation, testing and optimization of the shim, with the aim of creating a robust platform that enhances RINA's capabilities in the management of sensor networks and smart devices. The 802.15.4 shim will be integrated with the RINAsense architecture, which is a library for rapid prototyping of RINA sensors.

The result of a dimensionality reduction algorithm is typically plotted as a scatterplot. However, evaluating the effectiveness, and more concretely, comparing the results of different algorithms is difficult under this scenario. The goal of the project is the creation of an interactive tool that facilitates the visual comparison of 2D and 3D datasets resulting from the application of different dimensionality reduction algorithms. The idea is to use a 3D rendering library, such as deckGL to create an interactive application for the visualization of complex datasets, and the implementation of interactive tools for cross selecting elements in multiple views. 

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc

Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peña or apply directly:

https://webapps.bsc.es/recruitment/job/2443

The Accelerators and Communications for HPC Group leads cutting-edge research and development around accelerators/coprocessors in HPC and high-performance networking. We collaborate closely with the major vendors in the topic for HPC: NVIDIA and Intel. We organize locally international events such as the PUMPS Summer School, PATC Courses on CUDA/OpenACC, and annual hackathons, and collaborate in the organization of related international conferences and workshops such as SC, IEEE Cluster, or AsHES.

The group has been recently awarded one of the prestigious ERC Consolidator Grants. We are looking to cover a PhD Student positions to work on novel system architectural proposals applied to Homomorphically Encrypted Deep Learning Inference. Prospective candidates may take a look at the following paper to get an initial idea of our focus:

https://arxiv.org/abs/2103.16139

Group Web Page: https://www.bsc.es/discover-bsc/organisation/scientific-structure/accelerators-and-communications-hpc

Key Duties

• Drive research in the above-mentioned topic, under the supervision of the Group Manager and the mentorship of a postdoctoral researcher

Application

  Contact Antonio Peña or apply directly:

https://webapps.bsc.es/recruitment/job/2443

 The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case Data Center Networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

This offer includes a part-time employment contract at the BSC during the Master Thesis.

The System Tools and Advanced Runtimes (STAR) group focus on research crossing multiple software layers, from OS, runtimes and low-level APIs to programming models, tools, and applications. We aim to improve the system software stack to support increasingly complex workloads such as traditional HPC, AI and Data Analytics on massively parallel HPC and Cloud platforms. We develop and maintain the state-of-the-art OmpSs-2 data-flow programming model.

Runtime systems are a crucial component in exploiting current multi-core and heterogeneous systems. Our research on runtime systems focuses on Nanos6, the reference runtime implementation of the OmpSs-2 programming model. Nanos6 provides state-of-the-art task scheduling, dependency management, monitoring infrastructure and support for accelerators such as GPUs and FPGAs. We also work with the Linux kernel to extend it to better support advanced runtime systems.

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.

The complexity of game engines has significantly increased to exploit multi-core and accelerated systems. The goal of this project is to leverage the knowledge and tools from the High-Performance Computing domain to improve the programmability and performance of a game engine. The main idea is to apply the OmpSs-2/OpenMP data-flow programming model to orchestrate all the activities (DAG) performed by the game engine. The project will likely require extending the OmpSs-2/OpenMP programming model and runtime to fulfill the specific requirements of game engines.

Main planned activities:

• Port a relevant open-source game engine to OmpSs-2 programming model
• Evaluate its performance on several multi-core platforms
• Leverage OmpSs-2 interoperability support to integrate offloading (e.g. CUDA) and/or graphic (e.g. Vulkan) APIs into the game engine
• Identify and implement new features in the OmpSs-2 programming model and runtime that can ease the development of game engines or improve their performance

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)

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.

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.

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.

The project is a collaboration with cardiologists from Hospital Clínic de Barcelona that will provide the data and the specialized validation.

PETGEM serves a pivotal role in geophysics by solving Maxwell's equations using a high-order vector finite element method. However, to meet the evolving demands of geophysics research, this project aims to enhance PETGEM's functionality by integrating new features and adopting PETSc-DMPlex programming principles. The key focus areas include code refactoring and the integration of high-performance computing (HPC) principles.

Key Objectives:

1. PETSc-DMPlex Integration:
2. 1. Incorporate PETSc's data management API for unstructured meshes (DMPlex) into the PETGEM codebase.
   2. Enable PETGEM to leverage PETSc's interfaces for various common mesh formats, enhancing interoperability.
   3. Remove legacy dependencies to improve code modularity and maintainability.
3. Enhanced Portability:
4. 1. Enable PETGEM to seamlessly adapt to diverse computing environments by leveraging PETSc's capabilities.
   2. Improve portability by allowing PETGEM to work with PETSc's standardized mesh representations.
5. Performance Optimization:
6. 1. Utilize DMPlex's runtime mesh decomposition capabilities to enhance PETGEM's computational efficiency.
   2. Alleviate I/O bottlenecks by eliminating the need to read mesh decompositions from disk.
7. Documentation and Workflow Improvement:
8. 1. Provide comprehensive documentation for the enhanced PETGEM version, ensuring ease of use and understanding for researchers.
   2. Streamline the workflow for electromagnetic modeling in geophysics within PETGEM, enhancing user experience.
9. Contribution to Scientific Computing:
10. 1. Position the enhanced PETGEM as a benchmark in scientific computing and software engineering.
    2. Demonstrate the broader applicability of the project's outcomes beyond geophysics, showcasing best practices in code development.
11. Real-world Significance and Future Opportunities:
12. 1. Strengthen the collaboration with leading research groups to ensure the real-world relevance of the project.
    2. Open avenues for future research and development by establishing PETGEM as a foundation for advanced electromagnetic modeling.

The culmination of this master's project will be a refined, well-documented version of PETGEM, enriching the workflow for electromagnetic modeling in geophysics while contributing valuable insights to the broader scientific computing and software engineering communities. The collaboration with esteemed research groups enhances the project's real-world impact and opens doors to continued advancements in the field.

Mobile networks are continuously experiencing a profound transformation, driven by the emergence of groundbreaking applications and communication paradigms, such as the eagerly expected metaverse. In the coming years networks will face daunting challenges to meet the demands of these future applications. In this (r)evolution, Deep Learning is among the most promising techniques for achieving unprecedented levels of connectivity and user experience that can meet such demands.

In this project, you will have the opportunity to work on AI and machine learning applied to real-world problems around 5G and beyond networks. We will focus on hard open challenges, such as AI-based mobility management optimization and planning in future 6G networks, or creating cutting-edge network automation technology to facilitate the day-to-day operations of network engineers around the world.

Quantum computing is an emerging field poised to revolutionize information processing. Unlike classical computers that use bits to represent information as 0 or 1, quantum computers employ qubits, which can exist in superposed and entangled states. This enables more efficient solutions to complex problems, such as large number factorization or quantum system simulation.

However, quantum computing still faces significant challenges, including algorithm optimization. Many quantum processes require finding optimal solutions for optimization problems, such as minimizing system energy or determining molecular structures. This is where Deep Learning comes into play.

The goal of this research project is to explore how Deep Learning (in particular Deep Reinforcement Learning) techniques can enhance optimization in quantum computing. By applying deep neural networks, we can learn intricate patterns in quantum data and develop more efficient algorithms for solving optimization problems. These advancements could accelerate practical applications in areas like quantum cryptography, material simulation, and industrial process optimization.

You'll join an interdisciplinary team, since the work will be carried in collaboration with the Quantum Information Group at IMDEA Networks in Madrid. They will offer the expertise in Quantum  Physics while we work on the Deep Learning part..

 Additional details can be found in these papers:

 https://arxiv.org/abs/2310.01113

https://arxiv.org/abs/1910.07421

Internet services are known to collect large amounts of personal information. As a result, more than half of EU citizens are concerned about their online privacy. In this context, data brokers are companies devoted to collecting and selling personal information to other companies. Data brokers are often implemented as third-party trackers, which allow them to gain visibility across the Internet. Recent works show this personal information is not only used for targeted advertising, but also for more obscure practices, such as price discrimination, credit scoring, phishing or identity theft. The project addresses these growing privacy concerns by enhancing ePrivo, a new online privacy observatory. ePrivo will continuously scan the Internet to unveil third-party trackers, the tracking methods they use and the data brokers behind them. A proof-of-concept prototype of ePrivo is already available at https://eprivo.eu. In this project, we will deploy the ePrivo service in production, extend its capabilities for web tracking and phishing detection, and release it under an open-source license. The results will be useful to the RIPE community, including ISPs, network operators, Internet users, policy makers and researchers.

Cybersecurity is becoming an increasingly important challenge for all companies and individuals alike. While big names used to be the main targets in the past, as people's lives move online, anyone is nowadays a potential target for any kind of cyber-attack, ranging from phishing to ransomware or serious privacy issues. In order to fight against those ever-evolving threats, Machine Learning is increasingly being used behind the scenes to design better systems that are able of self-learning to boost detection rates and boost overall resilience to unknown attacks. As AI-based solutions penetrate products across the industry, a new kind of threat that is often overlooked is becoming more and more prominent and dangerous: adversarial machine learning (AML).

AML focuses on designing specific inputs to deceive a previously trained Machine Learning models into misclassifying them for a specific purpose. One of the main flaws of any state-of-the-art Machine Learning or Deep Learning algorithms is that they assume that the nature of the data they receive is systematically benign, which is generally the case but does not hold true when an adversarial input is received. The motivation behind altering a ML model into thinking that, for example, a new sample is benign when in fact is malicious can range from pure research to more serious real-life issues such as an autonomous car wrongly classifying a stop sign (and thus provoking a fatal accident) or a wrongly diagnosed disease because of a slightly manipulated magnetic resonance image.

This problem is no exception for Cybersecurity where companies wrongly assume that once the last AI-based product is deployed in their network, their employees are safe...

This offer includes a part-time employment contract at the BSC during the Master Thesis.

The System Tools and Advanced Runtimes (STAR) group focus on research crossing multiple software layers, from OS, runtimes and low-level APIs to programming models, tools, and applications. We aim to improve the system software stack to support increasingly complex workloads such as traditional HPC, AI and Data Analytics on massively parallel HPC and Cloud platforms. We develop and maintain the state-of-the-art OmpSs-2 data-flow programming model.

Runtime systems are a crucial component in exploiting current multi-core and heterogeneous systems. Our research on runtime systems focuses on Nanos6, the reference runtime implementation of the OmpSs-2 programming model. Nanos6 provides state-of-the-art task scheduling, dependency management, monitoring infrastructure and support for accelerators such as GPUs and FPGAs. We also work with the Linux kernel to extend it to better support advanced runtime systems.

The goal of this project is to port several existing SPEChpc applications to the data-flow model that OmpSs-2 provides. The student will be tasked with analyzing the existing parallelization of applications and its potential issues (load imbalance, global barriers, lack of overlap in communications/GPU kernels, etc.). Then, the student will solve these issues while porting the application to the OmpSs-2 programming model and Task-Aware libraries. Finally, the applications will be evaluated in the Barcelona Supercomputing Center's HPC clusters.

The student will have the support from researchers in the STAR group, which will provide help with performance analysis and OmpSs-2.

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

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.