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explored using scientific machine leaning. Machine learning, programming experience and a curious mind-set You are fascinated by how computers can learn from data and you have a strong interest in the
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microbiology and CMDs. You need to be ambitious, motivated, capable of working independently, and have good skills within written and spoken English. In addition, you should have experience with experimental
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programme, please see DTU's rules for the PhD education . Assessment The ideal candidate will have solid background in experimental molecular biology combined with experience with microbial protein production
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engineering and wave-wind theories is advantageous Experience in coding (e.g., Python) and in the use of Structural Analysis Software (e.g., OpenSees, Abaqus) is highly desirable Ability to work independently
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Materials Science and engineering or chemistry or related subjects such as corrosion science and engineering. Masters in electronics engineering with prior experience on humidity/corrosion reliability
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foods for the future? If you have knowledge within the area of allergy and protein-chemistry as well as have hands-on experience with cell-based in vitro models and are looking for a PhD fellowship
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section Energy Technology and Computer Science, where you will have around 20 colleagues with a mix of research and industrial experience. We work with research, innovation, technology implementation, and
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(120 ECTS points) or equivalent in Food Science, Materials Science and Engineering, Packaging Technology, or a related field Documented experience with food and bio-based materials chemistry
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experimental research in nanoparticle catalysis using advanced operando electron microscopy This collaborative PhD project between Technical University of Munich (TUM) ( the group of Prof. Barbara A.J. Lechner
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helping your colleagues. The group is part of the Section for Experimental and Translation Immunology at the Department of Health Technology . Our section has all the necessary infrastructure and comprises