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structures but also free energies. The work sits at the interface of statistical mechanics, molecular simulation, and deep generative modeling, and is embedded in SIMPLAIX, a research initiative on multiscale
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Publications in relevant research areas Open-source software contributions Experience with: Foundation Models Large Language Models LLM Fine-Tuning Agentic AI Systems Diffusion Models Scientific Simulation
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, including industrial applications in geology. Your immediate leader will be the Head of Department. Duties of the position Simulate MRI data and model disturbances arising from various physical sources
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involve selecting various event topologies to constrain background and perform detailed study of double-beta decays. GEANT4-based simulations will be used with the software FALAISE developed by SuperNEMO
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
-informed learning techniques; Development of simulation-based optimisation methodologies, including Bayesian optimisation, derivative-free optimisation, multi-objective optimisation, model calibration, and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 2 months ago
alternative mechanisms and distributed compliance. This PhD thesis aims to propose, model, and compare different mechatronic propositions through both simulation-based evaluation and experimental calibration
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | about 2 months ago
models). Your tasks Development of analytical theories of biomolecular phase transitions, focusing on liquid-liquid phase separation. Development of numerical simulation tools, e.g., for process modeling
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty
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of the position Simulate MRI data and model disturbances arising from various physical sources Develop, implement and train INR models for MRI time-series data Analyze functional MRI data of the spinal cord Present