14 bio-inspired-computing Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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using unique novel mouse models, spatial technologies and analytical methods. Postdoctoral Researcher in Functional Cancer Microbiome through the NORPOD program NORPOD is a collaborative postdoctoral
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-Cell Genomics through the NORPOD program NORPOD is a collaborative postdoctoral program of the Nordic EMBL Partnership for Molecular Medicine . The partnership is a network of four national research
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students. The Department of Physics (Faculty of Science ) is a large and multidisciplinary department that offers the most varied university programme in the physical sciences in Finland. At the beginning
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group, established in 2018 at the University of Oslo, has recently expanded to the University of Helsinki. The group specializes in developing computational methods to understand the regulatory mechanisms
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, cancer genomics, or a related field. Applicants with a background in biology or (bio)medicine are welcome to apply, provided they have documented expertise in computational biology Proficiency in
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well as newly developed computational methods. You will report your results in a scientific publication as a first author with the support of other members of the group. Skills, experience/qualifications
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of Social Science at the University of Helsinki, we offer an attractive and diverse workplace in an inspiring environment with a variety of development opportunities and benefits. The position as postdoctoral
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Sini Laakso’s lab Autoimmune Neurology at the Translational Immunology Research Program at University of Helsinki, Faculty of Medicine invites applications for the position of POSTDOCTORAL
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investigation and will develop an advanced computer modeling framework. By simulating processes at various scales, from the atomistic to continuum, we aim to reveal how temperature and saturation fluctuations
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offered a fully funded contract of up to 3 years. About the position The postdoctoral research position will require developing and applying cutting-edge machine learning methods to computer vision and