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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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at the Faculty of Medicine, University of Helsinki. The project will focus on using and extending deep learning-based approaches developed within the group to integrate bulk multi-omics cancer data. The Kuijjer
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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 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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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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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
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in biology, forest sciences, environmental sciences, or data sciences. A strong background or motivation to learn ecological genetics and evolutionary biology as well as competence in bioinformatics
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sustainable policies for human flourishing. This includes identifying social and environmental inequalities, supporting inclusive social action and policies, informing learning and education, promoting