888 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" positions in Sweden
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Field
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while
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experience) of their research career and must not have been awarded a doctoral degree. Where to apply Website https://umu.varbi.com/en/what:job/jobID:907734/ Requirements Research FieldBiological
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methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods. Special
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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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initiative started in 2018 with the purpose of advancing Swedish academia and industry to the forefront of quantum technology, and to build a Swedish quantum computer). For the research on quantum optics with
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) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks
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accordance with Karolinska Institutet’s template (http://ki.se/qualificationsportfolio) . You may change or add to your application at any time up to and including the application deadline date. After
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in at least two of the following areas (or similar): Wireless Communication Systems, Internet Systems and Computer Networks, Robotics, Machine Learning and AI, Automatic Control, or Mathematical
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome