64 development "https:" "https:" "https:" "UCL" "UCL" "UCL" Postdoctoral positions at Technical University of Denmark
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the turbulent fields and the fast-ion populations. Your mission will be to bridge this gap. The Project: A Diagnostic Revolution This innovative project aims to develop the world's first Photonic-Assisted
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active member of EE&SB and contribute to many ongoing projects Develop intellectual property and business cases We are looking for candidates who are highly motivated, self-driven and have demonstrated
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to exploit the full potential of algebraic curves in the construction and classification of locally recoverable (LRC) codes. The goal of CREATE is developing in a multidisciplinary team the most suitable
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Diffraction Microscopy (3DXRD), installed at the European Synchrotron Radiation Facility in France and also being implemented at other synchrotrons. We utilize the techniques to visualize the evolution
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Job Description We are looking for a highly motivated chemist with strong experience in chemical synthesis and electrochemistry to join our research and development activities. Would you like
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, this position offers a unique opportunity. You will work with real robots, real sensors, and real physical interaction problems, contributing directly to the development of autonomous mining systems capable
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translate cutting-edge concepts into experimentally validated devices. By joining us, you will operate at the absolute forefront of heterogeneous photonic integration, develop high-performance nonlinear and
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approaches are transforming how next-generation biologics are discovered and engineered. We are launching an ambitious research program centered on AI-driven generative protein design to develop GPCR-targeting
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the formation of anaerobic biofilms and granules, and on developing novel solutions to promote biofilm formation. The research work will have a cross-disciplinary nature and will involve a range of
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures