37 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" positions at University of Copenhagen in Denmark
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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project, should contact Principal Investigator prof. Mikkel Bille, mbille@hum.ku.dk , phone +45 35329480 Link to the department’s website: https://saxoinstitute.ku.dk Introduction PhD studies consist
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supervision and training of research fellows and other staff. The successful applicant must also teach, supervise, prepare and participate in examinations, and fulfill other tasks requested by the Department
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk
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here: https://employment.ku.dk/faculty/criteria-for-recognising-merit . Furthermore, we are looking for a highly motivated and enthusiastic scientist with the following skills and experience: Essential
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assessment database: https://ufm.dk/en/education/recognition-and-transparency/find-assessments/assessment-database . Please note that we might ask you to obtain an assessment of your education performed by
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information about the CIIR center’s activities and overall research agenda can be found at https://jura.ku.dk/ciir/english/about/ An assistant professorship is a further-education post. The main
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Attend PhD courses Write scientific articles and your PhD thesis Teach and disseminate your research To stay at an external research institution for a few months, preferably abroad Work for the department
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation