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Professor will be responsible for teaching undergraduate and graduate courses in computer systems and algorithm design on a temporary basis. Teaching may be in our core courses alongside other faculty
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a PhD in machine learning, math, stats, physics, or some other technical area by the time the position starts. Additional Qualifications Candidates should have significant experience in some area of
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to eligible team members. Learn more at https://hr.duke.edu/benefits/ Minimum Qualifications Education See job description for education requirements. Experience See job description for requirements. Degrees
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project “Analytics for Learning with Machines” (ALMA) The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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intelligence, data analytics, uncertainty analysis, probabilistic modelling, or statistical learning you have experience working on relevant research or project activities involving machine learning or data
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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Learning Contexts TLTED 5005 - Equity, Diversity, and Justice in Education TLTED 5108 - Teaching and Learning of Mathematics in Grades Pre-K - 5 MATH 1050 - Precollege Mathematics I MATH 1075 - Precollege
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administrative support primarily for the Senior Associate Dean of Research and Graduate Education, with some targeted support for the Associate Dean of Military and Veteran Partnerships, Business Officer, and the
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 5 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