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conferences and contributing to collaboration between NIBIO, NMBU, and national and international research partners. Professional qualifications (required) A Master’s degree in machine learning, artificial
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collation, mixed precision, multi-GPU; experiment tracking (W&B or MLflow), configuration management (Hydra), strict seed and artefact reproducibility. Graph machine learning. PyTorch Geometric or DGL
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is
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-related discipline. 2) Strong programming ability in optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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secondments of the PhD student at other involved parties in the network such as the company QuiX (Enschede) and UCL (London). Other trainings and collaborations in the area of quantum error correction