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with machine-learned quantum mechanical force fields trained on diverse chemical fragments. Sci. Adv.10, eadn 4397(2024) Where to apply Website https://ecolecentraledelyon.recruitee.com/ Requirements
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results
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Intelligence experience skills Python (for data analysis, automation, scripting, and simulations) Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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until obtaining a PhD Carry out research of good quality within the framework described above Academic publications and popular science dissemination Participate in the research group SIMLab (https
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | about 2 months ago
operates the first Czech quantum computer named VLQ. https://www.it4i.cz/ Activity description: · research and development of methods for acceleration of parallel applications in the High Performance
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, biochemistry, or other relevant discipline Excellent written and spoken English skills High degree of independence and commitment Good knowledge of python and C++ Experience in relevant work tasks are
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(advanced) programming skills in C/C++. Proficiency in Python and/or MATLAB is an added advantage. You are a collaborative team player with excellent communication skills. You demonstrate a high level of
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, predict, and manage them remains fragmented across disciplines. The Understanding and Predicting Impacts of Climate Extremes under Global Change Doctoral Network (CLIMES DN) (https://www.climes.se/climesdn
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, deep learning, and statistical modeling Strong programming skills (e.g., Python, R, TensorFlow/PyTorch) Experience handling large-scale ecological, biological, or remote sensing datasets Strong