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to teaching based on research in the subject at hand, subject didactics, as well as teaching and learning in higher education. Collaborative teaching capacity and the ability to interact with actors inside and
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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, spans the breadth of computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At
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Sweden through collaboration within supported projects, consultations, mentoring, and advanced national postgraduate courses. Engage in the strategic development of advanced machine learning and AI
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collaboration with experimental LNP PhD / PostDoc projects in the Barriga Lab and with M. Carroni at the Cryo EM facility SciLifeLab. Other responsibilities may include teaching, supervision and contributions
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP
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research across various areas, managed under the Department of Immunotechnology. Through the collaborative project L2Bridge, which is supported during five years by a donation (https://www.lu.se/artikel