247 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" Postdoctoral research jobs in Sweden
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for applications: March 17, 2026 Reference number: 1786-2026 URL to this page https://web103.reachmee.com/ext/I003/583/main?site=6&validator=e4575239eb8c0828707e2b716f86c5f8&lang=UK&rmpage=job&rmjob=9975&rmlang=UK
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
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that you will help us to build the sustainable companies and societies of the future. The research at the Division of Machine Elements is mainly focused on tribology and its applications. The research group
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to certain circumstances, such as sick leave, parental leave, military duties, etc. Evidence of expertise in cell biology/immunology/physiology and biotechnology, demonstrated through degrees, publications and
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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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is considered the official date of completion. Exceptions to the three-year eligibility limit may be made for documented circumstances such as parental leave, sick leave, or military service. What you
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently
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University? Find out more about what it´s like to work and live in Sweden. Where to apply Website https://uu.varbi.com/en/what:job/jobID:902324/type:job/where:39/apply:1 Requirements Research
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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted