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Field
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connect to our group’s work and how this position supports their career development goals. Possible research topics include (but are not limited to): Optimization algorithms for machine learning (stochastic
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under small temperature gradients (ΔT ≤ 30 K). Design and Optimize novel fabrication techniques to achieve high packing densities in thermoelectric generators (TEGs) for powering IoT sensor networks
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Applications are invited for several postdoc positions funded by the ERC (European Research Council) to work on the 'EFT-XYZ' (Effective Field Theories to understand and predict the Nature of the XYZ Exotic
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programming experience; apply by sending CV and transcripts to alanwar@tum.de About the Role: Join us to push the state of the art in formal verification and reachability analysis for discrete-space systems
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 1 month ago
] Subject Area: Cluster Algebras Appl Deadline: 2025/09/30 11:59PM (posted 2025/07/04, listed until 2025/09/30) Position Description: Apply 2025/09/30 11:59PM Position Description Cluster Algebras at MPI MiS
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: - QUANTITATIVE VERIFICATION: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification
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, a writing sample from previous research work (ca. 15 pages), as well as relevant certificates (all academic degrees). The application should be sent no later than the 17th of November 2025 via e-mail
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Mathematics (analysis, numerics, modeling) or in a comparable program with a strong mathematical focus and knowledge in, for example, functional analysis as well as the theory and numerics of PDEs. Strong
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The Max Planck Institute for Demographic Research (MPIDR) is recruiting highly qualified Post-Docs or more senior Research Scientists to join the Lab of Migration and Mobility and/or the Lab
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verification problem compared to verifying a neural network with millions of neurons. We will also use the feedback of the safety layer to improve the learning rate by excluding provably unsafe actions during