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that employ carbohydrate-based monomers, working towards the synthesis of new ‘smart’, sustainable and biodegradable materials. Responsibilities and qualifications The project will take advantage of enzyme
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working with “DTU Smart Road,” a full-scale pavement research platform at DTU’s main campus that hosts embedded strain and temperature sensors. Experiments will also involve the development and installation
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more postdocs will join the lab soon. The group focuses on the study of bacterial stress response and bacteria-phage interaction. For more information, please visit the homepage at UCPH . The group is a
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? The Villum Investigator group will (eventually) consist of three postdocs, three PhD students and will be part of the section for Theoretical High Energy Physics, Astroparticle and Gravitational Physics
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charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students working collaboratively
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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
on sustainable feedstocks. Supervise and mentor PhD students and postdocs. Drive national and international research funding applications. Collaborate with academic and industrial partners, including engagement
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defects and trapped charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students
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Administrator, an Administrative Supporter and approximately 10-15 PhD students, postdocs, and visiting scientists. Several POLIMA researchers have attracted prestigious personal grants, e.g., Villum Investigator
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University, and the University of Toronto to form a truly Pioneering Center on P2X. You can read more about the CAPeX research themes and X-trails, our organization, and the other open Ph.D. and postdoc cohort
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renewable energy sources into the power grid. Key research questions may include: How can machine learning be leveraged to improve the accuracy and speed of dynamic simulations in renewable power systems