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- Technical University of Munich
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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computing and GPU infrastructure for the development and evaluation of LLM-, VLM-, and agentic AI solutions Research across the entire automotive software development lifecycle, from requirements and software
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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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offer A Master’s thesis opportunity (6-12 months, or longer) at the intersection of AI and urban physics. Access to high-performance workstations and GPU resources for training large-scale models
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, microfluidics, laboratory automation, and GPU computing infrastructure. The opportunity to develop AI methods and scientific software that are directly deployed on cutting-edge experimental platforms. Vacation
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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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-electron density, its spatial confinement, relaxation behaviour, and the resulting modification of optical constants—providing the basis for next-generation advanced manufacturing steps for GPUs or CPUs. Be
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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