63 post-doc-in-wireless-communication-and-networks-2016 PhD positions at Technical University of Denmark
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develop protein-based solutions. Manage a multidisciplinary research team, including post docs, PhD students and Msc students. Supervise and guide the team’s research projects related to novel protein and
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with many opportunities for professional development and global networking. Responsibilities and qualifications We are seeking a PhD student with background and interest in enzymology, molecular biology
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loads — EV fleets, residential batteries, smart heat pumps, and data-center clusters — across distribution and transmission networks is critical to unlocking deep decarbonization and maintaining grid
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fish‑community dynamics and environmental conditions at offshore sites. The successful candidate will join the Observation Technology research group at DTU Aqua and work in a collaborative project with
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customization of Large Language Models for educational purposes. The ideal candidate must have a strong professional network and documented collaboration capabilities, alongside a proven track record in
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research and have excellent networking and communication skills in English. Furthermore, selected candidates will be expected to: Conduct excellent research and develop the field in one or more of the above
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, you will be investigating if the current metrics for accessing code quality are valid, and then you will be improving them using programming language techniques. Your goal will be to measure the effect
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experience with statistical tools (e.g. in R, MatLab, or Python) are expected. The team at DTU Aqua is highly international and knowing the Danish language is not needed. You must be available for boat-based
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learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key