11 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD research jobs in Denmark
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Computer Engineering, please visit https://ece.au.dk/ See more about our activities on LinkedIn: https://www.linkedin.com/company/au-ece What we offer The department offers: a well-developed research
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-species interaction in gas–liquid bioreactor platforms. Deliver scientific excellence through high-impact publications, dissemination and outreach. Teach and develop courses at BSc and MSc levels in
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
can read more about Center for Visualizing Catalytic Processes (VISION) at https://vision.dtu.dk . If you are applying from abroad, you may find useful information on working in Denmark and at DTU
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for teaching and research CV including employment history, list of publications (applicants applying for the position as senior researcher should indicate scientific highlights), H-index and ORCID (see http
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of publications (applicants applying for the position as senior researcher should indicate scientific highlights), H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching
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the Ph.D. candidate is enrolled as a Ph.D. student at Aarhus University Graduate School of Health ( http://phd.health.au.dk/ ) in a separate procedure before starting as a Ph.D. student. The successful
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, strong record of research publications, and demonstrated potential to excel in scientific research. You are expected to teach and instruct both graduate and undergraduate students as well as supervise
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: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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preparation for mass spectrometry analysis in the laboratory Learning of data analysis methods and code to understand mass spectrometry data Present your data at lab meetings and (inter-)national meetings