520 data-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" positions in Denmark
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like additional information about the position, please contact head of team Mattias Andersson on 93 51 18 01. To learn more about the department, visit DTU Wind . Please note that applicants may be
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mitigation, construction, and bio-based systems. Familiarity with integrating structured and open data practices in LCA workflows will be considered a plus. Additionally, you have a proven track record of
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to the faculty’s departments. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Professor Troels Skrydstrup, +45 28 99 21 32, ts
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to assess and interpret data from experiments, with the ability to identify trends, troubleshoot issues, and optimize processes. Experience in critical evaluation and troubleshooting of both process
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Job Description Are you passionate about environmental contaminants, food safety, marine ecosystems, and creating real-world impact through cutting-edge analytical and data-driven approaches? Do you
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information about the position may be obtained by contacting Peter Bollen (peter.bollen@sund.ku.dk) or Clara Prats (cprats@sund.ku.dk). Application Deadline - Submit your application with CV, references, and
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leading department of a highly reputed technical university, supporting cutting-edge research with robust, sustainable software and data solutions? Are you visionary and excited about developing a new
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on Department of Sustainability and Planning here. You may obtain further professional information from Associate Professor Ivar Lyhne, +45 5142 2310, lyhne@plan.aau.dk How to apply Your application
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channels to showcase success stories and opportunities. Team and administrative support: As part of the External Relations Team, contribute to shared tasks such as onboarding new staff, cost modelling, data
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will