100 computer-programmer-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "IMEC" PhD positions at Nature Careers
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules
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Biotechnology (https://www.sciencedirect.com/science/article/abs/pii/S0958166923000770?via%3Dihub ). Your responsibilities: You will develop and investigate fermentation processes with heavily reduced CO2
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lab with access to specialized equipment such as a P2 solo instrument which will be used to perform Oxford Nanopore Sequencing on site. We also have access to computational clusters which can be used
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motivated and talented predoctoral researchers. The focus of our research is in the field of Data/Computational Science in Astrophysics & Cosmology. You can find more information about our research area and
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of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https
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Call for applications We are pleased to announce the call for the second cohort of our Graduate Programme RNAmed – Future Leaders in RNA-based Medicine. Applications are invited for 11 PhD
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or a closely related field is required, or for a 4-year integrated Master’s and PhD program. Essential qualifications include: a strong motivation for fundamental research a solid background in particle
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine