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reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
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and experience: Qualifications PhD in Computer Science, AI, Machine Learning or related field Experience A strong research track record relative to opportunity, including the ability to produce and
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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., MATLAB, Python) is required. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding
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. The successful candidate will have the responsibility of developing, in collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 17 days ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Position in Machine Learning for Single-Cell Genomics (f
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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-changing technologies. Life-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a Postdoctoral
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models