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Field
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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consistent thermodynamic framework; Algorithm development for the numerical resolution of the resulting systems; Numerical simulations and validation of the proposed models. The model will be formulated in
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thermodynamic framework; Algorithm development for the numerical resolution of the resulting systems; Numerical simulations and validation of the proposed models. The model will be formulated in terms of gradient
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/Electromagnetics theory, design and simulations Machinelearning mathematics and algorithms Numerical methods Programming skills (Python, MATLAB, or similar) Strong analytical and problem-solving skills. Good written
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a focus. Traditionally, this is done through iterative algorithms (‘trial and error’). In this project, we aim to develop a radically different approach where the correct shape is computed using a 3-D
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26 Mar 2026 Job Information Organisation/Company Universitat Internacional de Catalunya Research Field Computer science » Programming Mathematics » Algorithms Researcher Profile Recognised
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science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
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estimation, calibration, and out-of-distribution detection. The PhD candidate will work on novel algorithms, theoretical insights, and large-scale empirical evaluations, with a strong emphasis on
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for both youth and police. You will collaborate with stakeholders, end-users, game designers and our CONTEXT science team to improve our virtual reality game and its biofeedback algorithms. You will perform
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale