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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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will estimate the Seebeck coefficient, the electrical conductivity, and the thermal conductivity, and assess how changes in molecular structure, packing, and doping influence these properties. Key tasks
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Processing Skills required: - Medical computer programming: python, 3D slicer, LCmodel (optional), FSL, spm, ants) - Artificial Intelligence skills and deep learning experience - Proficiency in Tensorflow