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-depth understanding of data and related algorithms, data analysis, and machine learning. Our cross-cutting theme is machine learning-enhanced computational engineering. You’ll have an excellent
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, education and way of working : MS, or PhD in Biological Sciences, Biochemistry or closely related field with typically 10+ (MS) or 4+ (PhD) years of postdoc or industry related experience. Strong
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of Assistant Professor, we seek highly talented and already distinguished postdoc researchers and teachers who have passed the postdoc stage, are at the beginning of their career paths, demonstrate scientific
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(land, aerial, marine, or hybrid) Distributed decision-making, task allocation, and worksite optimization under uncertainty Energy-aware and resource-efficient collaboration strategies for fleets
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, PyTorch, Keras, scikit-learn) and strong understanding of machine learning algorithms, deep learning architectures, and statistical methods Good skills in extraction of data from structured/unstructured
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experimental materials synthesis, characterisation or both, and optionally, with experience developing algorithms for accelerated discovery based on data collection from automated instruments. Practical
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curriculum vitae (including major research funding obtained, research leadership, list of supervised PhDs and postdocs, number of peer-reviewed articles, H-index, total citations and the database used, e.g
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of supervised PhDs and postdocs, number of peer-reviewed articles, H-index, total citations and the database used, e.g. Web of Science or Google Scholar) (maximum 10 pages) APPENDIX 2: Complete list