52 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at AALTO UNIVERSITY
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, please click here . Please see more of our Open Positions here. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/404806/doctoral-candidates… Requirements Additional Information
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and surface wettability and developing wetting characterization instruments. More information about our group can be found in: (http://physics.aalto.fi/smw ). Your experience and ambitions We offer a
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. We hold the highly valued triple crown accreditation by AACSB, AMBA, and EQUIS. The position is located in the Department of Information and Service Management, which consists of three disciplinary
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and transformation of technology-based business; data science and artificial intelligence; health technology; neuroscience; and software engineering. We award around 450 master’s and 80 doctoral degrees
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, please see the student information and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . How to apply? Please submit your application through
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Water and Wastewater Engineering. All positions are linked to the Digital Waters (DIWA) Flagship (www.digitalwaters.fi ) : more information on DIWA can be found below. We are now looking for three
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industrially relevant research and development. For the selected candidates, office space during visits, as well as the standard office facilities (e.g., computer facilities, university e-mail, access
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transcripts Contact details of at least two referees The deadline for applications is 22nd of March 2026, but the position will remain open until filled. For additional information, kindly contact Prof. Andrei
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information and an indication of the preferred starting date (max 1 page) CV including details of all academic outputs and publications (if available) Contact information for two referees (with email addresses
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transport simulation Travel behaviour and choice modelling Network optimization and algorithmic methods Machine learning and data-driven approaches for mobility analysis, prediction, or decision support