76 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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, especially in the context of scientific writing and presentations A collaborative mindset and enthusiasm for interdisciplinary research As a formal qualification, you must hold a PhD degree (or equivalent). We
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recruitment and supervision of future PhD students and postdocs. We seek applicants who: Hold a PhD in molecular biology, biotechnology, bioengineering, or related fields. Demonstrate enthusiasm for complex
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PhD students working collaboratively at the interface of physics and Earth science. This project offers a rare opportunity to engage in groundbreaking research with high impact across both academic and
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including supervision of BSc and MSc students associated with the project As a formal qualification, you must hold a PhD degree (or equivalent). In the assessment of the candidates, consideration will be
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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during the project period in courses at DTU. Candidates should have a two-year master's degree and as a formal qualification, you must hold a PhD degree (or equivalent) preferably culturing basidiomycetes
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development Structure–function analysis of enzymes Project coordination and team collaboration Scientific writing and publication As a formal qualification, you must hold a PhD degree (or equivalent). We offer
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formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific
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Job Description The Quantum and Nanophotonics Section at DTU Electro is seeking a highly motivated postdoc to be a part of a program on ‘Symmetry-guided discovery of topological photonics’, led by
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writing scientific papers. The developed models will be tested on data from energy investment models, as well as transport infrastructure problems. We will be an academic team of three PhD students and four