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entwickelt und betreibt das PTC Forschungsinfrastruktur für wissenschaftliche Arbeiten unter kontrollierten Umweltbedingungen und im Freiland. In der Greenhouses & Phytochambers Unit (GPU) betreuen
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, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13 position with an application deadline of August 14, 2026. About us Located in the prosperous capital of Bavaria and home
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Apply online now : https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=29547 Postdoctoral Researcher – Translational AI in Neuropathology (m/f/d) Stellenanzeige merken
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pipelines for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods
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Ginkgo (https://github.com/ginkgo-project/ginkgo), with the aim of improving the efficiency of scientific simulations in applications such as cardiac electrophysiology (MICROCARD: https://microcard.eu
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clinical datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be
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MSCA Doctoral Network ( https://www.elevate-dn.eu/ ) and co-supervised by our partners at the university of Liège. Your tasks in detail: Develop an event-driven learning algorithm for latent reasoning
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; while they are trained using GPU-based optimization methods, the actual information processing takes place without a GPU, making them energy-efficient and fast. Diffractive networks are to be designed
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<span lang="en">Universitätsklinikum Hamburg-Eppendorf</span> | Hamburg, Hamburg | Germany | 16 days ago
resources for travel and conferences
- Work on cutting-edge AI methods with direct clinical relevance
- Access to large-scale multimodal biomedical datasets and modern GPU infrastructure
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, containerisation, version control and service deployment on HPC systems or cloud infrastructure, including GPU-accelerated workloads Experience with deep learning frameworks, production model serving