72 modelling-and-simulation-of-combustion-postdoc Postdoctoral positions in Switzerland
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EPFL - Ecole polytechnique fédérale de Lausanne, Probability and Partial Differential Equations PROPDE Position ID: EPFLMATHSPROPDE-POSTDOC [#27478] Position Title: Position Type: Postdoctoral
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thinking with renewable energy community planning, developing methodological innovations that bridge quantitative modeling and qualitative approaches. Your tasks Develop and apply optimization and simulation
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, advanced materials, and innovative concrete strengthening solutions. Our work combines experimental investigations, analytical modelling, and numerical simulations to develop and validate novel reinforcement
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at the forefront of addressing several fundamental questions including: How can we best simulate strongly correlated quantum systems and harness the power of both classical and quantum computing resources? Can we
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, analytical modelling, and numerical simulations to develop and validate novel reinforcement systems. This position is part of an Innosuisse-funded collaborative project with industry partners, focusing
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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17 Oct 2025 Job Information Organisation/Company Academic Europe Research Field Medical sciences » Health sciences Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country
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description of the project(s) you are interested in pursuing as a postdoc 1-2 writing samples (e.g. a chapter of your PhD thesis, a publication, or an article-in-progress) The names and contact details of up
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their careers in a diverse and inspiring university environment. We offer attractive employment conditions in an intellectually stimulating environment (employment category: POSTDOC 5). The position is initially
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research. Your position Design, implement, and evaluate large-language-model (LLM) pipelines for synthetic data (fine-tuning, retrieval-augmented generation [RAG], prompt engineering). Plan and analyze