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push the boundaries of green innovation and contribute to a healthier planet, we invite you to apply and become part of a dynamic research team at the forefront of sustainable extraction technologies
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materials built by covalent linking of monomers, supramolecular polymers are dynamic and formed by the self-assembly of monomeric units brought together by non-covalent interactions. The connections between
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, interdisciplinary approach aims at relating the atomic-scale structure, dynamics and functions of single nanoparticles in both thermal catalysis and electrocatalysis, aiming to advance the understanding of catalysis
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. This Ph.D. project is focused on experimental investigation and realization of advanced quantum photonic devices, based on crystal-phase structures in nanowires. This is a recently developed technology
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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partners are: Visibuilt (a start-up company), DTU Sustain, and Munck Asfalt (a road construction company). Project description and tasks This PhD project includes a combination of modelling, coding, field
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tools such as RFdiffusion2 or BoltzDesign. Perform molecular dynamics simulations and in silico screening to assess inhibitor-target interactions and predict selectivity. Clone, express, and purify top
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experience with carbohydrate-active enzymes is prioritized. You must be well organized, structured, self-driven and enjoy interacting and collaborating with colleagues including PhD students, postdocs, and you
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to the high-temperature PEMFC to produce warm water for practical applications (e.g., heating and washing) in disaster areas. Investigate the thermal dynamics and overall performance of the HT-PEMFC stack and system under
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML