100 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" research jobs in Finland
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processing, machine learning, statistics or related fields. Demonstrated expertise in ML/AI, with prior experience of applications in the healthcare domain, particularly in cancer research considered a strong
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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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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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across all areas in Computer Science, including Algorithms and theory Bioinformatics and digital health Computing systems and networks Cybersecurity Human-computer interaction Machine learning and
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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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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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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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intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and
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more detailed description of the project can be found here: https://invest.utu.fi/projects/ Applicants are expected to have strong and versatile methodological skills, including multilevel models (mixed