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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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global water contamination through advanced materials science. Your Role: Investigate the structural and chemical dynamics of molecular adsorption processes using advanced characterization techniques
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, University of Copenhagen. We are located at Taastrup campus, Copenhagen. We offer creative and stimulating working conditions in dynamic and international research environment. Our research facilities include
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, we have a dynamic exchange of international researchers, who stay at the department for a shorter or longer period. For more information about the Department of Management, please visit: http
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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
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for sustainable production of food by applying your microengineering and material science skills? Here, we can offer you a unique opportunity to do exactly that in a dynamic research environment. In the SOLARSPOON
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PhD Scholarship in Biodiversity changes of marine flora and fauna associated with ecosystem resto...
of eelgrass also lack other 3D structures as stone reefs to support fish and other mobile fauna, because stone fishery for constructing harbors and other infrastructure has been intensive for hundreds of years
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will be part of the section of Artificial Intelligence, Cybersecurity, and Programming Languages (ACP), an active, dynamic research group that combines bold research and a collaborative spirit. The
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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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CMOS implementation of event-based processing for edge-AI applications. The project will explore: SSM formulations adapted to spiking dynamics SSM implementation for time-series classification