Consulta ofertes d'altres estudis i especialitats
This project aims to analyze the prediction capability of Optical Coherence Tomography Angiography (OCTA) images for Diabetes Mellitus (DM) and Diabetic Retinopathy (DR,) in a large high-quality image dataset from previous research projects carried out in the field of Ophthalmology (Fundacio¿ La Marato¿ TV3, Fondo Investigaciones Sanitarias, FIS). OCTA is a newly developed, non-invasive, retinal imaging technique that permits adequate delineation of the perifoveal vascular network. It allows the detection of paramacular areas of capillary non perfusion and/or enlargement of the foveal avascular zone (FAZ), representing an excellent tool for assessment of DR.
A more detailed description of the project can be found in
https://www.cs.upc.edu/~eromero/Downloads/Retina-TFM-Project-01.pdf
The project is proposed in collaboration with Javier Zarranz Ventura
(Institut Clínic d'Oftalmologia, ICOF, Hospital Clínic de Barcelona, and
Institut d'Investigacions Biomèdiques August Pi I Sunyer, IDIBAPS),
which would provide a large annotated database to develop the project. For further information, please contact Alfredo Vellido (avellido@cs.
upc.edu) or Enrique Romero (eromero@cs.upc.edu).
We want to demonstrate experimentally that augmenting a model with fNIRS data carries neural activity features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of fNIRS attention masks.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures changes in oxygenated (HbO2) and deoxygenated hemoglobin (HbR) in the cerebral cortex. Due to its portability and low cost, fNIRS has been used in Brain-Computer Interface (BCI) applications, characterizing hemodynamic responses to varying stimuli, and investigating auditory and visual-spatial attention during Complex Scene Analysis (CSA). In this project, we want to design and implement an fNIRS study with a goal of studying the impact of neural and BCI outcomes to improving the training of LAI models' attention mechanism (e.g., Transformer attention) during reading comprehension tasks (e.g., the participants will be judging the quality of generated text). We want to demonstrate experimentally that augmenting a model with fNIRS data carries neural activity features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of fNIRS attention masks.
The candidate will:
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.
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
Lung cancer remains the leading cause of cancer-related deaths worldwide. AI has recently emerged as a transformative tool for enhancing medical decision-making. However, its widespread adoption faces several challenges, including data quality, model transparency, and interpretability. This thesis seeks to explore how innovative AI techniques can revolutionize lung cancer research and treatment, offering new opportunities to address these challenges. It aims to contribute to the broader application of AI in healthcare.
This project offers the candidate a unique opportunity to apply artificial intelligence techniques to real-world challenges in lung cancer research and treatment. As part of this thesis, the candidate will work with datasets, including patient records, genetic data, molecular alterations, treatment outcomes, and exposome data. These datasets will serve as the foundation for developing AI models that address critical challenges in lung cancer treatment, such as predicting patient outcomes and identifying optimal treatment strategies.
The candidate will focus on the following core tasks:
Data Exploration and Preprocessing: The candidate will gain experience in handling complex medical datasets by cleaning, preparing, and structuring the data to ensure it is suitable for advanced AI analysis.
Building AI Models: Using machine learning and deep learning techniques, the candidate will develop models aimed at predicting lung cancer progression, evaluating treatment efficacy, and understanding the impact of various environmental and genetic factors.
Interpretability and Explainability: A significant emphasis will be placed on making AI models interpretable and transparent. The candidate will explore techniques to ensure that the models produced are not just accurate but also explainable, providing healthcare professionals with clear insights into the model's predictions and decisions.
Exploring Interaction Networks: The candidate will analyze interaction networks, studying relationships between patient genetics, environmental factors, and treatment responses to identify key drivers of lung cancer outcomes.
Throughout the project, the candidate will not only gain hands-on experience with cutting-edge AI tools and methodologies but also develop a deeper understanding of AI's role in healthcare. This project provides an impactful opportunity to contribute to a field where AI innovation can directly improve patient outcomes.
In the proposed project, we are interested in the mobile setting, and propose the use of depth information, on top of the usual RGB (Red, Green, Blue) pixel data acquired by mobile device cameras, to track and quantify visual attention.
Automatic gaze tracking methods have been used in the recent years for various purposes, including measuring ad attention or enabling for gesture-less user-device interactions. Most of these methods have been tested in the desktop setting, while the few reported attempts that addressed the mobile setting report low accuracy and require continuous calibration. Additionally, the aforementioned methods rely on expensive sensors, such as infrared eye trackers, that limit their scaling capacity. Other methods employ touch-based interactions (e.g., tracking zoom/pinch gestures and scroll activity) to produce an estimation of the user gaze which, at best, are weakly correlated with visual attention. However, 42% of the time spent on websites is by mobile users, while similar trends are reported for the percentage of page views per visit, which creates an opportunity for a novel attention measurement technology to take root; one which can offer accurate, reliable, and scalable measurements.
In the proposed project, we are interested in the mobile setting, and propose the use of depth information, on top of the usual RGB (Red, Green, Blue) pixel data acquired by mobile device cameras, to track and quantify visual attention. Depth information can either be estimated with post computer visual processing or acquired directly with high precision, using advanced LiDAR sensors that newer smartphone models are currently equipped with. Furthermore, we propose the use of "lightweight" Deep Learning (DL) techniques to achieve an accurate and robust gaze point estimation without the need of extensive calibration. Unlike prior art, we will consider the use of historical information to inform future predictions and will examine how such models can be tailored to each user by injecting personalized data. In addition, we propose a more lightweight Neural Network (NN) architecture, suitable for the processing power of mobile devices, that also leverages the use of video or photo frames with depth sensory data.
The candidate will:
We want to demonstrate experimentally that augmenting a model with eye tracking (ET) data carries linguistic features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of ET attention masks.
Eye movement features are considered to be direct signals reflecting human attention distribution with a low cost to obtain, inspiring researchers to augment language models with eye-tracking (ET) data. In this project, we want to investigate how to operationalise eye tracking (ET) features, such as first fixation duration (FFD) and total reading time (TRT), as the cognitive signals to augment LAI models' attention mechanism (e.g., Transformer attention) during training. We want to demonstrate experimentally that augmenting a model with ET data carries linguistic features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of ET attention masks.
The candidate will:
Recent advancements in nanotechnology have enabled the concept of the "Human Intranet", where devices inside and on our body can sense and communicate, opening the door to multiple exciting applications in the healthcare domain. This thesis aims to delve into the computing, communication, and localization aspects of the "Human Intranet" and how to practically realize them in the next decade.
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 work on developing AI schemes (based on graph neural networks or multi-agent RL) for the detection and localization of events inside of the human body. Data will be gathered with an in-house simulator that integrates mobility models (BloodVoyagerS) and communication models (TeraSim).
This thesis aims to explore the possibilities of the new and less studied variant of neural networks called Graph Neural Networks (GNNs). While convolutional networks are good for computer vision or recurrent networks are good for temporal analysis, GNNs are able to learn and model graph-structured relational data, with huge implications in fields such as quantum chemistry, computer networks, or social networks among others.
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:
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.
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
Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, quantum computing faces many challenges relative to the scaling of the algorithms and of the computers that run them. This thesis delves into these challenges and proposes solutions to create scalable quantum computing systems.
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 computing systems and the algorithms that run within them, 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:
This work is included in the research project LABINQUIRY, in a teaching environment. The goal is to develop a system capable of collecting and organise documents (pairs question-answer) expessed in natural language. These documents are generated within the interaction between the teacher and the students.
LABINQUIRY project aims to develop a didactic infrastructure to help secondary school and university teachers to implement inquiry-based learning processes called study and research paths (SRPs). The starting point of an SRP is an open question that students address in small groups with the help of the teacher, they have to search for data, information, new tools and knowledge, etc, study them and jointly elaborate a single answer of the whole class.
One difficulty posed by the SRPs in terms of management is the teacher's treatment of the weekly reports produced by the student groups with the partial results of the work done during the week. The teacher responsible for the SRP has to "process" these reports in limited time in order to prepare the next class session. In this processing, the teacher has to read the reports - about 10-12 per class -, compile the most important information and group them according to the type of questions raised by each team, the data collected, the interim results provided, and the sources of information used.
The research group responsible for the LABINQUIRY project is interested in achieving an automatic processing of the content of student reports. The aim of the TFM would be, from a set of 10-12 reports, to collect and organise the information contained in the reports to help the teacher organise the next task. Both the questions posed by the teacher in the SRP and the students' reports are natural language texts. The collection and organisation of the information can be done at different levels and from different points of view (some of which are already fixed but some of which are not), giving flexibility to the work to be done during the TFM. In principle, the techniques to be used would fall into the following areas: unsupervised learning (clustering) and natural language processing (word embeddings).
The expected timing is January-June 2024.
To carry out this project it is advisable to have a good mastery of Spanish and/or Catalan and the techniques mentioned above.
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