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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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scienceYears of Research ExperienceNone Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria Degree : PhD in computer science, machine learning, or computational
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creating exciting opportunities for machine learning to address outstanding biological questions. The PhD, to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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of Research ExperienceNone Additional Information Eligibility criteria Education - PhD in education, cognitive psychology, occupational psychology/ergonomics, educational technology, or a related field. - Dual
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers