206 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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quantum light sources coupled to quantum memories. Quantum memories are key components of optical quantum computers and scalable quantum networks: They are responsible for the central tasks of storing and
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this date. Job description Recent improvements in machine learning have dramatically improved the accessibility of de novo protein design. In this project, we seek to exploit these developments to interrogate
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relevant (e.g., teach and co-supervise PhD and MSc student projects). Dissemination of your research through publications in “top rank journals of the field ” and attendance at conferences. Qualification
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characterization. At SurfCat, we make extensive use of surface science techniques and collaborate closely VISION, which provides access to advanced microscopy tools. Promising catalysts may also be studied at large
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are looking for two new postdocs. You will join a new research program on the role of universities and entrepreneurial ecosystems in building deep tech startups and scaleups. You will work towards building a
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meeting the criteria for both positions are encouraged to apply for both positions separately. Project description Many adults suffer from hearing and vision (‘dual-sensory’) loss. Because these losses
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scientific areas. We educate both Bachelors and Masters of Science in Engineering and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD
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Development and Demonstration Programme. The position is affiliated with the Section for Mechanical Technology and will be under the supervision of Associate Professor Jonas Sundberg. The project focuses
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and PhD students working on various aspects of computational biology and hosts a number of collaborations with the Hospital. BiRC hosts the genomeDK supercomputer, which will be available
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Anatolia and from the Tigris to the Mediterranean with a focus on visual culture. Qualifications Applicants must hold a PhD degree in Classical Archaeology or closely related disciplines and be able