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intelligence, or similar) is essential for the PhD focused on ‘Technology’. Candidates should be highly self-motivated, able to demonstrate initiative and have a desire to learn, and able to work as part of a
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. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocation Basic Qualifications Qualifications: - PhD with substantial hands-on research experience in biomedical sciences is required
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. To learn more about the Princeton University Art Museum, visit artmuseum.princeton.edu . Responsibilities Exhibition and publication research Writing and editing label copy Other exhibition and
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PhD candidate on Interprofessional Learning and Team Resilience through the Electronic Health Record
-proof healthcare? This PhD position offers the opportunity to explore how interprofessional teams can use insights from the Epic electronic health record to learn together, improve care, and strengthen
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or related disciplines. A Doctoral/PhD degree will be an asset for consideration; Proven ability to teach in English for the courses/domains mentioned in the “Duties” section; Experience in providing academic
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will strive to continually excel as an innovative world-class university that makes a positive impact on society, living up to the University’s motto: “To learn and to apply, for the benefit of mankind
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. Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, J. Chen, A deep learning-enhanced framework for multiphysics joint inversion, Geophysics, 88(1), K13-K26, 2023. https://doi.org/10.1190/geo2021-0589.1 [3
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Job Description DEPARTMENT OF COMPUTER SCIENCE Research Assistant Professor in Computer Science (3 vacancies) (23240526) The Department presently offers BSc, MSc, MPhil, and PhD programmes, and is
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Job Description DEPARTMENT OF COMPUTER SCIENCE Research Assistant Professor in Computer Science (3 vacancies) (23240021) The Department presently offers BSc, MSc, MPhil, and PhD programmes, and is
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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation