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opportunities means you will grow and learn, having the chance to participate actively in staff trainings and development projects based on your interests and needs. We value work-life balance and well-being in
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for immersive data simulation. Supportive, diverse, and inclusive research culture. Our wide range of professional development opportunities means you will grow and learn, participating actively in diverse
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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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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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. The department has a strong community on related topics: research groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will
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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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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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, 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