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Postdoctoral Researcher in Multimodal Machine Learning for Precision Cancer Medicine The Machine Learning in Biomedicine (MLBioMed) research group at the Institute for Molecular Medicine Finland
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decade, providing opportunities to mine big data to learn more about the drivers of AMR in humans. Our work includes computational analysis of antibiotic resistance and microbiomes, statistical analysis
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to state-of-the-art resources and excellent support to further learning and professional development The University of Helsinki offers comprehensive services to its employees, including occupational health
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data has increased massively in the last decade, providing opportunities to mine big data to learn more about the drivers of AMR in humans. Our work includes computational analysis of antibiotic
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to conduct scholarly work and research both independently and as member of interdisciplinary research groups have ability of learn fast have motivation and capabilities to teach environmental life cycle
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skills and willingness to teach in the Faculty’s teaching programmes. A post doc teaches normally two courses per academic year. Qualification requirements PhD degree in law. Teamwork skills and the
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to teaching or supervision duties. Requirements The successful applicant should have a doctoral degree in statistics, mathematics, machine learning, or other relevant field, and experience in developing and
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skills Competency in or willingness to learn process-based modelling Strong data management skills and proficiency with analytical tools e.g. Matlab, R, Python Previous experiences with eddy covariance
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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy