33 machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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                Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications 
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                supply, cooling, or shielding/activation. Proficiency in neutronics codes (OpenMC, MCNP, Serpent) or thermo-mechanical tools (ANSYS, COMSOL); or capacity for rapid learning. Ability to maintain disciplined 
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                or related fields. Preferably strong interest and foundation in immunology, immune cell assays, or translational research. Preferably experience with computer-guided protein engineering. Experience in human 
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                results at conferences, Teach and supervise BSc and MSc student projects, and be co-supervisor for PhD students You must have a PhD in macro-energy system modelling or a similar field. Strong coding skills 
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                experience in developing software for processing SURE and ULM data Have a desire to advance the field and help PhD students learn We offer DTU is a leading technical university globally recognized 
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                of structural response models against measurements. Support the structural dynamics and multi-body modelling efforts in HAWC2. Teach and supervise BSc and MSc student projects and be co-supervisor for PhD 
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                regional crisis both in terms of abundance, diversity, and biomass. Importantly, insects form a natural treasure chest for studying animal development, learning, and behaviour, controlled by receptors 
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                BSc, MSc, PhD to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk. Technology for people DTU develops technology for people. With our 
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                scientists. Publish research findings in high-impact academic journals and present at international conferences. Teach and supervise BSc and MSc student projects and be co-supervisor for PhD students. Your 
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                willingness to learn the complementary skill set. Experience in collaborative research environments. Excellent communication and teamwork skills. A proactive attitude towards learning and applying new skills