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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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Professional Experience: PhD and at least 3 years postdoc experience in relevant areas of expertise. Personal Competences: Diligent, enthusiastic, ability in theory and experimental physics. Summary
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, nuclear physics and technology , Phenomenology , Physics High-Energy Theory / Collider physics , Neutrino physics , Particle Physics , Astroparticle Physics Appl Deadline: 2025/11/30 11:59PM (posted 2025
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knowledge. The focus of the project can be defined in collaboration with the supervisor, with potential avenues including optimization aspects, parameter-efficient adaptation methods, model merging theory
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Infrastructure? No Offer Description Research line: Learning in single cells through dynamical internal representations. Job description: Develop theory, models and algorithms for identifying molecular encodings
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, Physics, Computational Chemistry, Nanoscience, Chemical Engineering, or a related field. Strong background in modelling (electro)catalytic processes using periodic density functional theory (DFT) is
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networks and transformers. Practical experience with density functional theory (setups, convergence, interpreting outputs). Strong Python and deep-learning stack (preferably PyTorch); good software practices