205 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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considered an asset in these positions. Applicants must also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab
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. Given the international focus of the degree programmes, the successful applicant will be expected to teach in English as well as Danish. Qualifications Applicants must have a PhD degree or document
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. theses at the interface between structural engineering and machine learning. You will disseminate your research through peer-review publications and participation in international conferences. You will
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activities within the areas of embedded software. The position requires a PhD degree within a relevant area (e.g. software, computer, or control engineering) and the desired candidate is expected to have
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linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
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, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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of the PostDoc project is to performreal-world testing of the shoulder device in healthy individuals and patients with upper limb movement disorders, as well as to develop the machine learning
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forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods