36 experiment-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in Denmark
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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the hiring process. The technical development of the virtual reality set-up is handled by project partners. Your role will be to help design and carry out experiments using AI and VR, define conditions and
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Experience working with unique, real-world health data Collaboration with an interdisciplinary research team across data science, mathematics, and health Skills highly relevant for careers in academia
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be motivated to conduct in-depth empirical research in close interaction with the port sector. Solid qualitative methodological skills are required, and prior experience with qualitative data
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Electrical Engineering, Energy engineering, or a related field (or about to complete) with a strong background in power electronics. Knowledge and experience in power electronics, electronic packaging, PCB
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relevant to your project. The programme also includes a mandatory requirement to gain experience in teaching and/or other forms of research dissemination and knowledge exchange. As part of the application
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programme, you will complete PhD courses within topics relevant to your project. The programme also includes a mandatory requirement to gain experience in teaching and/or other forms of research dissemination
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excited about the intersection of machine learning, AI security, and real-world deployment. The ideal candidate has experience or interest in one or several of the following: A solid foundation in
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experience in handling and analyzing large-scale genetic datasets are highly desirable. Familiarity with genetic association studies and the development or use of analytical pipelines for genomic data is an
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), and absolute sustainability concepts such as planetary boundaries, net-zero, and regenerative performance. Experience with quantitative environmental modelling, LCA, or related methods is an advantage