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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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., computational learning theory, expressiveness of transformers and other models, xAI, formal methods) or equivalent; willingness to conduct research on artificial intelligence/machine learning, combined with
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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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teaching and learning in higher education and get the opportunity to learn Swedish through the University’s Swedish language courses for new international staff. As you prepare to move to Sweden, we offer
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. Selected postdocs will work with a primary mentor on projects at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and
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identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon the successful completion of a background
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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