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23 Dec 2025 Job Information Organisation/Company University of Siegen Research Field Computer science » Computer architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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classical calibration pipelines, including camera models, calibration targets, and algorithms, followed by an overview of our in-house specialized methods. In parallel, you will work in experimental setups
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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago
description: The Scientific Computing Center is the Information Technology Center of KIT. The Research Group Exascale Algorithm Engineering of SCC works at the interface of algorithmics, parallel computing, and
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) The Institute of Computer Engineering (ZITI) at Heidelberg University invites applications for one research assistant position with the possibility to do a PhD in parallel at the chair of computer architecture
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 28 days ago
of subgrid models in simulations, in particular for feedback processes Creation of new reference cosmological simulation models of galaxy formation Quantification of modelling uncertainties in simulation
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Max Planck Institute for Evolutionary Biology, Plön | Plon, Schleswig Holstein | Germany | 29 days ago
theoretical models and computer simulations. Adaptation of complex traits is assumed to occur through subtle frequency changes at many loci following a shift in the trait optimum, i.e. polygenic adaptation
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program generation and optimization Verification, testing and security of software systems Explainability of AI and of software engineering Software for distributed, highly-parallel AI systems Intelligent
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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training