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application! Your work assignments This PhD position focuses on methodological and computational development in cryo-electron microscopy (cryo-EM), with emphasis on image reconstruction, volumetric analysis
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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. Position You will work actively on the preparation and defence of a PhD thesis focusing on machine learning-based forecasting of renewable energy production, with a particular focus on wind energy. The
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. This position offers an exciting opportunity to work in a dynamic, innovative research environment. Labor Contract: https://ucnet.universityofcalifornia.edu/labor/bargaining-units/px/index.html Lab: https
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. The researcher will develop novel research that applies advanced data science, machine learning and deep learning to various different data modalities. An ambition of this team is to implement predictive modelling
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after thermal treatment to maintain gas flow and optimal detection. The PhD student will learn the synthesis of nanomaterials and their integration with 3D printing techniques. The synthesized materials
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research findings, and contribute to the supervision of PhD and/or Master’s students. Undertake scholarly postgraduate coursework teaching of an exemplary quality, including coordinating units, developing
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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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of Informatics. You will be part of Visual Intelligence and the DSB group. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-deep-learning