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models, signal processing methods, artificial intelligence (AI) tools, and optimization algorithms grounded in electromagnetic (EM) principles. The project is inherently interdisciplinary, bridging
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: Neural networks and machine learning. Algorithm. Professional Experience: In the use of Python (PyTorch, TensorFlow) and C for the development and optimization of deep learning algorithms. Experience in
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, and Lifelines. Groups of patients with different prognoses will be identified through unsupervised clustering using algorithms such as K-means and NMF. To evaluate the tumor microenvironment, tools
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: Own the optimiser: design, implement, and tune heuristic/metaheuristic algorithms (e.g
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background in computer science or biomedical engineering, with a strong focus on programming using deep learning libraries and machine learning algorithms. Demonstrated experience in medical image processing
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these data structures and their associated algorithms to enable pattern-matching and other operations required, for instance, to infer microbial transcriptional regulatory networks through comparative genomics
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offer a PhD student position at the Universitat de Barcelona (as part of the PhD Program in Mathematics and Computer Science) to develop new AI and federated learning algorithms for diagnostic imaging in
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their creation. Functions to be developed: Build a Reinforcement Learning system that supervises and adjusts text generation through inputs and outputs. Develop automatic model selection algorithms
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processes with a focus on atmospheric applications. Contribute to the development and implementation of mathematical models and numerical algorithms. Analyze data from numerical simulations, climate models
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: Artificial Intelligence Models. Deep Learning. Development and implementation of the Model Predictive Path Integral algorithm: MPPI. Professional Experience: Development of perception and localization systems