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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human
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to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven
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new methods. Good computer skills. Additional qualifications for the position are: Documented theoretical or practical experience in structural biology and/or mass spectrometry. Experience of project
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consequences of higher host specialisation in the tropics – the role of ecological and evolutionary processes, and of data bias), and the successful applicant will work in the Evonets lab (evonetslab.github.io