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are seeking a highly motivated researcher with background in statistics, bioinformatics, machine learning, or computer science, with an interest in developing novel methods for analyzing ecological data
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position are expected to have a PhD degree in a relevant field (e.g. computer science, data science) and either the topic of the PhD research was GNSS and the applicant has experience in deep learning
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machine learning methods for the interpretation of large molecular and clinical datasets in close collaboration with medical research teams and biobanks. As a core facility and coordinator of the nationwide
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, is a leading Computer Science research and teaching unit in Finland. The research themes of the Department cover machine learning and algorithms, computer networks and distributed systems, software
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shows a good degree of scientific independence, interest in health-related application of machine learning models, and an aptitude towards developing new methods. The candidate will be part, and attend
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the merger of cutting-edge Climate, Social and Computer Sciences (CouSCOUS)”. The candidate will work with city modelling and deep learning methods for predicting traffic flows. The position provides
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(MIN), industrial co- supervisor Enrolment in Doctoral degree: University of Turku, Finland Target degree: PhD/D.Sc.(Tech.) in Doctoral Programme of Mathematics and Computer Sciences (MATTI) Degree
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and machine learning methods for the interpretation of large molecular and clinical datasets in close collaboration with leading medical research teams and biobanks. As a core facility, we also support
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. With this aim, PLENOPTIMA joins five of the strongest research groups in nanophotonics, imaging and machine learning in Europe with twelve innovative companies, research institutes, and a pre
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potential and scientific knowledge. The spearheads of our research and learning are technology, health and society. Read more: www.tuni.fi Gamification Group (GG) https://www.tut.fi/Gamification at Faculty