161 parallel-computing-numerical-methods "Fields Institute" Postdoctoral positions in Denmark
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The Institute of Mechanical and Electrical Engineering at the University of Southern Denmark (SDU) invites applications for a 2-year position as a postdoctoral researcher within spintronic computing
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Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in...
Chemistry at the Institute of Materials Chemistry, TU Wien. The group develops and applies electronic-structure theory, machine-learning methods and predictive calculations of defects and transport properties
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Institute of Mechanical and Electrical Engineering at the University of Southern Denmark (SDU) invites applications for a 2-year Postdoctoral Researcher position within the newly funded QUANTRONIC
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chemistry. The successful candidate will be employed by Aarhus University and primarily stationed in the Research Unit of Theoretical Chemistry at the Institute of Materials Chemistry, TU Wien, in the group
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science and philosophy. The REMAX project on Rigorous Evaluation Methods for AI Explainability, funded by a Villum Synergy grant, aims to create methods for explainable AI and their rigorous evaluation
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the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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. This is a fixed-term position to end 24 months after the start date. Job description The postdoc position focuses on physics-driven machine learning methods for ground-based, airborne and drone-based
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with Poleno Jupiter instruments, with data obtained with DNA methods and data obtained with a Hirst trap. • Numerical models used by the group covers the particle dispersion model HYSPLIT in
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-based surrogate models. Experience with high-performance and parallel computing. Familiarity with digital twins and real-time deployment of predictive models is an advantage. Who we are The two
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partners at UniProt, SIB Swiss Institute of Bioinformatics, and Aalborg University. The project aims to unlock decades of scattered QTL knowledge from the plant genetics literature by combining AI-assisted