182 parallel-computing-numerical-methods "Institute of Computer Science" Postdoctoral positions in Denmark
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Department of Computer Science at Aarhus University (Denmark) invites applications for a 4-month Postdoctoral Research Fellow position with focus on Cryptography. The starting date is November 1st
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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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-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
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research, currently
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physics and experience with quantum many-body scars is an advantage. Experience within numerical methods for quantum many-body systems is required. Personal and collaborative skills are also important
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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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PostDoc in aerobiological modelling Are you interested in atmospheric science and can you contribute to the development of a new generation of numerical models describing concentrations and
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, iterate on the synthesis of the novel permanent magnet. Qualified applicants must have: PhD degree in physics, astronomy, engineering, computer science or similar. Experience with micromagnetic modeling
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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