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biodiversity assessments and methodological development at Aarhus University. Your profile Applicants should hold a PhD in ecology, population or conservation genetics, evolutionary biology, bioinformatics, or a
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong communication skills in English and good knowledge of French
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including a list of publications Names and contact information for at least 3 references (Letters of reference may be requested of finalists) Your qualifications should include (by time of hire) PhD
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for methodological development in the project centres around demonstrating the feasibility of the use-cases with minimal data-sharing and via methods that concentrate on anonymisation, federated learning, and data
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or equivalent and a PhD (or close to completion) in computer science, math or comparable, or an applied/life science (e. g. engineering, biology, medicine) with a focus on data analysis and/or machine learning
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Post Doctoral Researcher in Human-centred Large Language Models for Software Engineering, Departm...
The Section for Software Engineering and Computing Systems, at the Department of Electrical and Computer Engineering (ECE), invites applicants for a two-year postdoctoral position within the area of
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for new research projects. Qualifications We are seeking a highly motivated candidate with the following qualifications: A PhD degree within biology, environmental chemistry, chemistry, or similar
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expression libraries. Is Your profile described below? Are you our future colleague? Apply now! Education You hold a PhD in biochemistry, bioengineering, microbiology, or a related field, with strong expertise
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intelligence (AI)-assisted image analysis for bioinformatics and medicine. The project is highly interdisciplinary, involving areas of microfluidics, fluidic mechanics, biomedical imaging, and machine learning