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details, the following tasks will be carried out in parallel: 1. The PhD student will extend the current database (see, J. Chem. Theory Comput.2025, 21, 8010−8033) by obtaining highly-accurate reference
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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in AI to study natural and artificial minds in parallel, creating the opportunity to make discoveries about ourselves and to find new ways to understand and improve AI systems. Appointments will be
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application of ecological attributes to paleoecological data Molecular basis of repeated adaptation to challenging soils Parallel evolution of clonality in angiosperms The Genomic Basis of Partial
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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computational biology, data mining, human-computer interaction and experience, machine learning, meta-heuristics, networking and mobile computing, parallel and high-performance computing, software engineering
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clinicians; (b) support medical clinicians who have developed and continue to sustain an academic career in parallel with their clinical responsibilities; (c) support the ongoing development
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collaborating experimental research groups. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based
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cell biology, microfluidics and microscopy. Previous experience in one of these fields will be appreciated, good experimental skills in general and a taste for meticulousness are essential. In parallel
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both