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Field
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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outside the box can communicate in English at a good level is welcome to apply. We value enthusiasm, self-motivation, and a positive attitude. As a formal qualification, you must hold a PhD degree (or
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, Computational Biology, Applied Mathematics or related field Interest in interdisciplinary research Applied experience with machine/deep learning methods Ability to handle multiple projects in a dynamic
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for a hard-working candidate. Qualifications Required skills A PhD degree within statistical genetics, applied mathematics, computer science, medicine, psychology, molecular genetics, or similar. Advanced
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, Bioengineering, Biophysics, Biostatistics, Mathematics, Statistics, Electrical Engineering, Biomedical data science, etc; Candidates with multidisciplinary backgrounds are also welcome. Strong skills in
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Technology and Production Automation, m.cao@rug.nl We are looking for candidates who meet the following requirements A PhD degree in Control Engineering, Applied Mathematics, environmental Psychology or a
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expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will focus on ambitious, ‘blue sky’ research for novel methods development
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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, systems biology, applied mathematics, nuclear medicine physics, or a related field. Prior experience in at least one of the following is a requirement: physiologically based pharmacokinetic (PBPK) modeling
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, Machine Learning, Physics, Mathematics, Engineering, or equivalent A solid publication record with research publication(s) in peer-reviewed international journals Good programming skills (ideally in Python