499 data-"https:"-"https:"-"https:"-"https:"-"https:"-"University-of-Rijeka" positions in Denmark
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] that process information in temporal rather than spatial modes to reduce their footprint. The project involves a collaboration between DTU Electro (Senior Researcher Mikkel Heuck) and Harvard University (Dr
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
on “Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data
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reliability. Prospective applicants for this PhD proposal should have the following qualifications: M.Sc. degree in communications engineering, mathematical engineering, electrical engineering, computer
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Job Description We are looking for a skilled and experienced Full tack Developer with interest in designing and building tools to work with dietary and nutrient data. The Research Group
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March 2026 (23:59 Danish time). Open the “Apply now” link, fill out the form and attach your motivated application, CV and exam certificates. If you would like additional information about the position
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professional experience At least 5 years of hands-on experience as an entrepreneur or in building and operating digital products and technology startups, ideally within fintech, SaaS, data platforms, or AI
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academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme
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for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national