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for data processing and analysis will be used. The fellow will also take part in modeling and simulation activities, as well as in the dissemination of results. Specific tasks include: A2. Data Collection
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numerical models of rainfall–runoff and open-channel hydraulics in complex urban and rural settings to quantify flood depths, velocities, and risk; evaluating culvert/bridge performance and conveyance
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theoretical and computational soft-matter physics and biophysics. The successful candidate will work primarily with Dr. Matej Kanduč on research topics related to molecular dynamics (MD) simulations
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Maudsley Centre for Children and Young People: https://www.kcl.ac.uk/research/pears-maudsley-centre which will offer ample of opportunity to collaborate with academics, clinicians and lived experience
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skills in generative models applied to the design of bioactive peptides, with a strong focus on integrating these methods into the IA4Farma ecosystem. The work will include collecting and preparing peptide
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research area MERGE (https://www.merge.lu.se ), focused on climate modelling. Aerosol research has been conducted at Lund since the 1970s and is now a designated profile area at LTH (https://www.lth.se
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The Postdoctoral Researcher will join a new interdisciplinary project developing an AI-augmented decision science platform to understand and model high-stakes human judgments. This position will be jointly mentored
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experiments Perform high-speed optical and thermal diagnostics Simulate electric fields and develop mechanistic models Publish high-impact research outcomes Job Requirements: PhD in a relevant engineering or
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contributions in one or more of the following key areas: computational modeling of chemical systems, AI-driven materials discovery/design, robotics for chemical synthesis, machine learning applications in
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 21 days ago
dynamics simulations of biomolecules in solution – using models of about 100 000 atoms – that provide a data set to train stochastic low-dimensional models (Langevin equations), based on diffusion on a