74 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" PhD positions in Denmark
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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments
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or interest in runtime reconfiguration techniques and system safety considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an
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design and laboratory experimentation, you will first explore a broad range of sodium-oxide glass compositions using advanced computer models to predict how well ions can move through them. Based
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for a curious and engaged colleague with a genuine interest in the research topic. You enjoy working analytically with quantitative data, approaching research questions thoughtfully, and learning new
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preparation for mass spectrometry analysis in the laboratory Learning of data analysis methods and code to understand mass spectrometry data Present your data at lab meetings and (inter-)national meetings
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be advancing new materials for use in molecular biology. This entails synthesis and in vitro testing. The project is interdisciplinary in nature, and the successful applicant will acquire a diverse set
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Denmark was established to create value for and with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive
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collaborative environment that supports innovation, knowledge sharing, and professional development. Learn more about AAU Energy at www.energy.aau.dk . How to apply Your application must include the following
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on Problem Based Learning, with projects making up half the teaching at all semesters. Your competencies A relevant educational background within biology or a similar area is required. We seek a candidate with
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speech at the pinnacle of complexity. Like human babies, songbirds learn their vocalizations early in life from a social tutor. Numerous parallels to human speech learning, including analogous neural