74 algorithm-development-"The-University-of-Edinburgh" Postdoctoral positions at Technical University of Denmark
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meetings Potentially participate in Arctic field campaigns Be working with large data sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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optimization, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured
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to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and technologies. This is an exciting
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Job Description Nanomaterials and Nanobiosensors Group at DTU Healthcare Technology invites applications for a postdoctoral position to develop portable diagnostic devices and in vivo biosensors
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reaction evolution mechanisms for magnesium-based binders, leveraging state-of-the-art experimentation and numerical modelling tools. This position is part of the prestigious Villum Synergy project
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for Biomedical Quantum Sensing which aims to revolutionize biomedical imaging and diagnostics by developing novel microscopes based on quantum-enhanced measurements. Joining our team means taking a leading role in
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Job Description If you are excited by the challenge of understanding extinction through cutting-edge computational approaches, this postdoc offers a unique opportunity. You will help develop a
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such as improved texture, emulsification, or water-holding capacity—key features for developing innovative plant-based food products. The yeast will be converted into high-value, nutritious, and functional
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this project, we will develop neural diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also