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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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work is studying how undergraduate STEM courses can be tailored to meet the needs of all learners. Current projects involve studying STEM instructor perceptions of student-centered learning as
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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develop and evaluate semi automated machine learning techniques for content analysis adapted to handwritten documents and early printed books. These approaches will build upon existing optical character
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implement state-of-the-art data science principles into dental practice. While our primary focus is on the use of deep learning in (dental) imaging, our work expands into any type of data (e.g. tabular data
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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participate in cross-disciplinary research and development projects related to learning engineering and AI in education, working with a team of postdoctoral researchers, PhD students, and Master’s/undergraduate
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. Required Qualifications: Outstanding verbal and written communication and organizational skills Willingness to learn new experimental and computational skills Strong interpersonal skills, motivated to lead a
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disciplines including aerospace, combustion, design, fluid mechanics, materials, mechanical, mechatronic and robotics engineering. To learn more about the School click here We are currently seeking a
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 29 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact