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-Holstein (UKSH), is seeking a PhD student with a strong background in statistics, machine learning (ML)/artificial intelligence (AI), or bioinformatics. Our group develops novel computational and statistical
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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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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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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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characteristics. The insight will be used to assess global deep sea carbon turnover in the past and presently. Experience in lipid biomarker analysis, microbial cultivation, statistical modelling or machine
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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