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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
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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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. The project already includes existing datasets and established pipelines, and the successful candidates will contribute both by analyzing and extending these resources and by developing new data and approaches
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. Applications are invited for a full-time Postdoctoral Researcher in development of plasmonic nanopore for single molecule sequencing by surface enhanced Raman spectroscopy (SERS) position under supervision by
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occupational health care and health insurance, sports facilities, and opportunities for professional development. University assists employees from abroad with their transition to work and life in Finland (https
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positions. Benefits We offer you responsible and interesting tasks and the opportunity to develop your professional skills in a versatile operating environment. You get a professional and inspiring team
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development (https://www.helsinki.fi/en/about-us/careers ). How to apply A response to essential criteria (max 2 pages). Please ensure you provide demonstrated experiences from your previous work in relation
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research group is associated with the newly established Center of Excellence in Neutron-Star Physics (https://neutronstars.fi ), providing us long-term funding, strong connections to related Finnish and
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ecosystem carbon balance, including processes such as photosynthesis, respiration, and the export of dissolved organic carbon. Your work will contribute to developing an integrated, multi‑site understanding
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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