67 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at UNIVERSITY OF HELSINKI
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website at https://www.helsinki.fi/en/faculty-medicine/about-us/work-us How to apply Applications should be submitted through the University of Helsinki Recruitment System via the "Apply now" button
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teaching experience and pedagogical training, the ability to produce learning materials, additional teaching merits, a demonstration of teaching skills if required, and involvement in doctoral education
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on the ENERPOL home page https://cordis.europa.eu/project/id/101226229 Project description The successful applicants will join a multidisciplinary EU-funded Marie Skłodowska-Curie International PhD training
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international staff and/or about the university as an employer: https://www.helsinki.fi/en/university/working-at-the-university.https://www.helsinki.fi/en/university/working-at-the-university. . The Faculty
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and life in Finland. More information here: https://www.helsinki.fi/en/about-us/careers/welcome-finland-information-arriving-staff . A diverse and equitable study and work culture is important to us
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(€) List of publications List of selected 20 publications Instructions, requirements for language skills and models are available on the Faculty’s website at https://www.helsinki.fi/en/faculty-medicine/about
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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 an inclusive university community. We encourage
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ownership of open-ended problems The following are seen as advanteges but not necessary: Experience working with unstructured data sources (e.g. documents, long-form text) Familiarity with machine learning
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data library Apply diverse data science and machine learning methodologies, including the development of novel analytical approaches. Work and communicate efficiently in a highly interdisciplinary
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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