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for the hydrogen workforce. Project Focus This Postdoc Research Aims To: Examine how remote labs and digital learning environments can accelerate skill development and reduce time-to-job for hydrogen professionals
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, postdocs, students, and collaborators. Support postdoctoral lifecycle activities, including onboarding coordination and administrative updates. Assist with planning and executing the division's weekly
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extensive knowledge on zooplankton imaging techniques ability to program and train machine learning models for automated image classification experience with shipborne campaigns and ready to join multi-week
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Renewal”, and it is fully funded for 3 years. The project is conducted in close collaboration with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence
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of the postdoctoral scholars. https://postdoc.ucsd.edu/postdocs/px-contract.html Qualifications Basic qualifications (required at time of application) Doctorate degree or equivalent terminal degree in Engineering
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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expertise in machine learning or computational modelling who are eager to advance conceptual innovation toward practical industrial deployment. Qualifications PhD in Computer Science, Machine Learning
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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of the researchers of the DKM group are also affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) . The candidate is expected to join Integreat and strengthen the interdisciplinary
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective