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activities (approximately 16% of working time) and supervising PhD students in applied economics research at the University of Helsinki. In addition, the researcher can use up to 50 per cent of their working
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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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organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
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have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
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applied industrial economics with ample opportunities for individual and collaborative research, acquisition of external research funds, and supervising MSc and PhD students. We seek candidates who have
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funds, and supervising MSc and PhD students. We seek candidates who have completed a PhD in economics or agricultural economics, who have solid training in econometrics and applied microeconomics and
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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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The position requires: A doctoral degree relevant to the project (e.g., biology, genomics, evolutionary biology) Less than seven years since obtaining the PhD Ability to conduct independent scholarly work Strong
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volumes of audiovisual data is essential. The appointee must have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large
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for your next career stage. Your profile Recent PhD in Cell Biology, Neuroscience, Biochemistry or a related discipline. A strong academic track record, and at least one recent first-author publication