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Field
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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Description Primary Duties & Responsibilities: Lead the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and
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, advancing bioinformatics-driven research, facilitated by exceptional computational infrastructure including a centrally administered high-performance CPU and GPU cluster and network storage. This includes
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the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and hardware accelerators. Interact and collaborate
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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software stacks, including GPU clusters and related computing resources. This position will also play a key role in designing and implementing new features for research projects. The successful candidate
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experience. PACU/Critical Care/GPU experience strongly preferred. Experience working with patients 6 weeks in age to 25 years of age preferred. Licensure/ Certifications: Current Massachusetts license as a
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 17 days ago
-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers to help you balance work and family life Further training opportunities
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and model generation, point cloud rendering, visual effects (GPU shader, shadergraph, VFX) and 3D scene design Development of AR/VR applications What you bring to the table Full-time student at a German
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
pipelines and infrastructure, including data ingestion, preprocessing, model serving (via GPU/TPU), and continuous performance monitoring. Integrate commercial and open-source LLMs (e.g., OpenAI GPT, Claude