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above) grades. You have a strong background in deep learning. Previous experience with robotics, world models, reinforcement learning or other ML-based techniques for robot control is considered a plus
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related to modeling and simulation of biological systems, 3) very good IT skills, in particular the ability to program in Python, 4) very good knowledge of machine learning methods, neural networks, and
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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction, and integrate them
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the Defense Innovation Network Assistant (DINA) initiative. The position conducts applied and translational research in machine learning, natural language processing (NLP), retrieval-augmented generation (RAG
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is