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with national and international teams of mathematicians and computer scientists PhD applicants must possess a Master's degree in mathematics, theoretical physics, computer science, or a related field
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Position Title: Postdoctoral Research Associate in Crop Genetic Resources and Informatics Appointment Type: Post Doc/Trainee Job Description: Summary of Duties and Responsibilities: We are seeking
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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throughout their evolution. Our vision combines ideas from differential geometry, numerical analysis, scientific computing, dynamical systems, and applied mathematics to develop new mathematical frameworks
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the link between mechanics and biology in the musculoskeletal system, including related pathologies and repair of skeletal tissues. Experimental studies, tissue characterisation and computational simulation
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the order of its four building blocks, we can make DNA and RNA fold into precise nanoscale machines, carry out logical computations, and store and read out information. We are looking for one or two
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Postdoc Positions Application Deadline 31 Jul 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description A postdoctoral position (3 years, with possible extension) is available
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 8 days ago
Job Offer from September 17, 2026 The “Computational Relativistic Astrophysics" division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute; AEI) in Potsdam, led by
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and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods. Experience working with large-scale datasets and high-performance computing