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Field
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, from fundamental theory, laboratory experiments, and detailed numerical simulations, to mesoscale pore network modeling and upscaling to continuum-scale theories that can be applied in application
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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activity of individual neurons and mesoscopic population signals. His team develops open software for analysis and data management that forms the technical basis of the proposed project. The PhD candidate
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skills (e.g., numerical methods, statistical analysis, coding, data management) Good communication skills Ability to work in a team Driving license category B desirable (access to field sites) Your Tasks
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mantle in order to determine if this composition is the same as that of the nebula or the solar wind material. In the project we investigate the degassing processes of magmas experimentally and numerically
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systems. To get an overview of our lab's recent projects, take a look at our publications: https://drescherlab.org/publications.html We are looking for a highly motivated and talented PhD student to join
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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thesis for assessment prior to the application deadline will also be considered. It is a condition of employment that the PhD has been awarded. Research-level experience with numerical analysis and partial
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the experimental lab (https://www.mathbioleiden.nl/software.html#virtualleaf ). Incorporate detailed insights into the model of the mechanical properties of cell walls based on experiments and small-scale
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radionuclides. The successful candidate will combine laboratory investigations, radioanalytical techniques and numerical modelling to improve our understanding of radionuclide behaviour under relevant