9 learning "https:" "https:" "https:" "https:" "https:" "https:" "HFML FELIX" Postdoctoral positions at SciLifeLab
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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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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learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
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KTH Royal Institute of Technology, School of Engineering Sciences Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics at KTH and Science for Life Laboratory is a
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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commitment, make Uppsala University one of Sweden’s most exciting workplaces. Read more about our benefits and what it is like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/ The position may
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are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it’s like to work and grow at KTH. Trade union representatives
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constellation of SciLifeLab researchers and infrastructure units. This position is embedded in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab. Our lab develops deep learning models
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues