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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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machine learning techniques, and GPU programming. The simulation results will be compared to observational data obtained using facilities worldwide including ESO and NOT. Who we are looking for A successful
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the research environment: Faculty of Agriculture and Forestry, University of Helsinki https://www.helsinki.fi/en/faculty-agriculture-and-forestry Viikki Campus https://www.helsinki.fi/en/about-us
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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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research in a supportive and stimulating environment Access to state-of-the-art resources and excellent support for further learning and professional development The University of Helsinki offers
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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opportunities for professional development and continuous learning extensive occupational health care a broad selection of staff training staff discount on lunch in our campus restaurants, as well as on a range
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, University of Helsinki https://www.helsinki.fi/en/faculty-agriculture-and-forestry Viikki Campus https://www.helsinki.fi/en/about-us/campuses/viikki-campus Careers at the University of Helsinki https
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will support your move to Jyväskylä and Finland. How to apply? To apply, your ORCID profile (https://orcid.org ) must be up to date and your publications must be visible to the public (Set visibility
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the University of Helsinki and living in Finland, please see https://www.helsinki.fi/en/about-us/careers . A diverse and equitable study and work culture is essential to us. That is why we do our best to promote