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will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
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development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
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Researcher (R1) Positions Postdoc Positions Application Deadline 10 Aug 2026 - 20:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Dec 2026 Is the job
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on various aspects along the battery value chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoc project Atomistic modelling and synthesis
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and validate its accuracy against previous benchmarks for small molecules like PtH. This approach is general and can be directly combined with EOM-CC embedded in point charges, or in periodic DFT
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proposals; etc. What We're Looking For Basic Requirements: Knowledge and experience typically acquired by: 3 years in postdoc position and PhD or equivalent degree PhD in Chemical Engineering, Chemistry
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and validate its accuracy against previous benchmarks for small molecules like PtH. This approach is general and can be directly combined with EOM-CC embedded in point charges, or in periodic DFT
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lifetimes of spins on surfaces. This approach combines electronic states obtained via a periodic quantum embedding (i.e., equation-of-motion coupled-cluster in periodic DFT, pbcEOM-CC) with a coarse-grained
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization