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interactions that determine formulation stability and performance. The research will employ atomistic molecular modeling grounded in statistical mechanics to investigate binding thermodynamics and molecular
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Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related quantitative field. A solid background in machine learning, statistics and/or mathematics. Strong programming skills in Python
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interdisciplinary, and together we contribute to science and society. Your role We seek a highly motivated AI scientist, biostatistician or computational biologist who is well versed in the statistical and machine
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SD-25045 – PHD IN HONG-OU-MANDEL INTERFERENCE AND ENTANGLEMENT WITH COLOUR CENTRES IN SILICON CAR...
formally through the HR system. Applications by email will not be considered. Application procedure and conditions We kindly request applicants to provide their nationality for statistical purposes only, as
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conditions We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce
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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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estimation, and the detection and compensation of sensor drift and degradation. The candidate will develop data processing and modelling approaches combining signal processing, statistical analysis, and data
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. Applications by email will not be considered. Application procedure and conditions We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics