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clinical data, high-end GPU resources, and integration into TWIN-X, an EU Horizon Europe consortium with 18 partners from 12 European countries. You will work with data from TUM University Hospital and
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description: GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt operates one of the leading particle accelerators for science. Currently, the new FAIR (Facility for Antiproton and Ion Research) one
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datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be supervised by
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, e.g., git, automated testing, packaging, documentation and code review. Experience with GPU computing or high-performance computing. Interest or experience in LLMs, tool-using agents, or agentic
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funded doctoral position (TV-L E13) International research environment Joint affiliation with Saarland University and DFKI Access to state-of-the-art GPU and HPC infrastructure Collaboration opportunities
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the dynamics of single particles as a function of the swimming mechanism, the tortuosity of the media and the applied external flow. Investigate the behavior of dense systems with hydrodynamic interactions and
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state-of-the-art control techniques for particle accelerators, this PhD project has a clear practical objective: to define the optimal SRF cavity control approach for the HDC upgrade of EuXFEL, currently
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 2 months ago
well as the downstream steps, where the product is separated using centrifugation, filtered, and re-dissolved. Online monitoring of the particle size distribution provides the basis for process control and optimization
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, optimization, or high-performance computing is highly desirable Experience with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered