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prospective clinical data. You will perform multiome analyzes and use machine learning methods. The position involves independent research in own defined projects and to work independently with writing
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of absence because of illness, parental leave, position as elected representative in trade union organisations, military service or service/assignments relevant to the subject area, this time can be deducted from
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. The successful applicant will employ computational fluid dynamics (CFD) and data-driven methods, such as machine learning, to study thermal runaway, venting processes, and the microscale mechanisms that can lead
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Biology Laboratory (EMBL), Institut Laue-Langevin (ILL), France, the International Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical
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of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work will include biomedical and biophysical research. It will also
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of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work will include biomedical and biophysical research. It will also
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on leading research and good teaching and we have access to equipment for advanced computer calculations and experimental measurements. You will work in a group of researchers who are motivated by
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skills Strong interest in programming languages and program analysis Good knowledge of mathematics Interest in machine learning, especially LLMs Assessment criteria Selection for third-cycle studies is
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. The PhD position involve projects aimed at breast cancer genomics, bioinformatic pipeline development, machine-learning, and integrative analyses of different -omics technologies (DNA, RNA, epigenetics) and
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application of UAS are conducted around a number of different aspects; including system integration, mission planning, autonomous flight, human-machine interaction, communication technology, aerodynamics