48 postdoc-molecular-dynamics PhD positions at Technical University of Denmark in Denmark
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dynamic community of PhD students and actively supports diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge
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subjects and engineering scientific disciplines. The teaching and research cover separation processes, bioprocesses, bioengineering, bioconversions, reaction engineering, dynamics and process regulation
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qualifications we are looking for: Excellent knowledge and practical experience on current molecular microbiology methods Experience with genomic and transcriptomics data analysis is beneficial. Experience with
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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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holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement
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packaging, one that’s safer, smarter, and more environmentally responsible? We are looking for a highly motivated PhD student to join our dynamic, interdisciplinary research team. By joining us, you'll gain
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renowned research group for Gut, Microbes, and Health at the National Food institute, Technical University of Denmark (DTU). We offer a dynamic and sociable research environment with exiting challenges and
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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
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI