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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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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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the physical mechanisms of infection. We are based at KTH and SciLifeLab in Stockholm (https://hannebellelab.org/ ). We are seeking a PhD student to study the biophysics of schistosome skin infection
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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
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and deep learning frameworks such as PyTorch and TensorFlow; Excellent communication skills in oral and written English; Creativity, thoroughness, and a structured approach to problem-solving; Good
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SciLifeLab community both locally in Uppsala and in Sweden at large. For information about the SciLifeLab fellow program, see https://www.scilifelab.se/research/#fellows . SciLifeLab Fellows are also part of a
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providing advanced bioinformatics support, infrastructure and training to the Swedish life science research community, and participating in international collaborations. Within NBIS, you will be part of
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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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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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or senior staff scientist. More information: https://www.scilifelab.se/researchers/simon-koplev/ Qualifications Requirements A doctoral degree or an equivalent foreign degree. This eligibility requirement