52 web-development "https:" "https:" "https:" "Fraunhofer Gesellschaft" Postdoctoral research jobs in Finland
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biology, multiomics and single cell methodology to drive projects focusing on modelling leukemia and immune cell dynamics with the goal to develop new personalised medicine approaches. As our new
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the History, Culture and Communications Unit at the Faculty of Humanities, the University of Oulu. The hired researchers will develop and implement their own research plan in the specified research area and
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facilities, and opportunities for professional development (https://www.helsinki.fi/en/about-us/careers ). YOUR PROFILE PhD in biology, mathematics, or a related field Strong background in mathematical
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will conduct econometric analyses using register-based firm-level data, contribute to the design and implementation of empirical research, prepare peer-reviewed journal articles aimed at publication in
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focus on internationally high-level research specifically in generating societal impact of wellbeing research by analyzing, developing, and disseminating effective evidence-based interventions, practices
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within the research project “From Clay to Code: Investigating Microbial Evolution in Response to Human Influence” funded by the Helsinki Institute of Life Sciences (HiLIFE). The project aims to establish a
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environment affiliated with the European Laboratory for Learning and Intelligent Systems (ELLIS) Institute Finland. The selected candidate will contribute to the development and implementation of precision
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and the services they provide. The successful candidate will play a key role in developing and applying economic models to assess impacts of biodiversity change on ecosystem service provision
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning