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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.

In recent years the volume of information available electronically has increased exponentially, coining the term Big Data to refer to this phenomenon. The medical domain is an area in which the number of documents generated by the centers for patient primary care constantly increases. However, a bottleneck is generated because processing these documents requires specialized personnel craftly performing tasks. In the framework of TAIDAMED research project, we are developing a set of processors that allow automatic analysis of medical texts taking into account criteria of robustness, high precision and coverage. In particular, this thesis would aim at the acquisition of patterns of clinical behavior from medical reports represented as semantic graphs using Neo4J database.

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.

Opground connects job opportunities with programmers, automatically searching, applying and passing all the first interviews for them; so that companies receive immediately the most appropriate profiles and can swiftly contact the best fitting profiles. Candidates only need to interview once with our intelligent Chatbot (called Opi) simulating the first interview in a selection process, where they explain their knowledge, skills and preferences.

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.

Ciencia de los Datos e Inteligencia Computacional Avanzada

This work is developed in collaboration with the IDIBELL (Institut d'Investigació Biomèdica de Bellvitge), with the main goal of identifying the role of cancer gene drivers into the Breast Cancer prognostic and the relapse patient prediction. Objective A classical statistical approach to identify biomarkers associated with human disease outcome is based on applying Cox regression models and Kaplan-Meier survival analyses. For example, the expression of many genes has been associated with cancer patient survival or outcome based on such analyses.

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.

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.

Consulta ofertas de otros estudios y especialidades