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Field
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simulations and thermal comfort analyses by developing fast parametric algorithms and data-driven surrogate modelling approaches capable of predicting dynamic outdoor thermal comfort with high accuracy
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the simulator and sensors located in the operating room (3) Develop data acquisition algorithms and data processing algorithms within the simulator (4) Develop forecasting algorithms (5) Raise alerts in
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” because early abnormalities are missed by current diagnostic methods. This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost
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electrochemical phenomena. · Propose non-invasive diagnostic methods based on this digital twin. · Develop algorithms to speed up or standardize the determination of the parameters of this digital
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable
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strong foundation in programming (e.g. Python) and core concepts in machine learning or data analysis. Ability to engage with research literature and develop analytical, problem-solving, and algorithmic
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, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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summary Join an international team developing scalable algorithms to solve numerical linear algebra challenges on supercomputers. Modern high-performance computing increasingly relies on hardware