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Field
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Models to push the frontier where computer vision, physics simulation, and embodied AI converge. Join Us! This position is part of a collaborative research programme between the University of Amsterdam
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in English (both verbal and written) and computer skills required. DEPARTMENT SPECIFIC FUNCTIONS: Design and execute independent research projects related to cardiac biology, heart failure, and
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intellectual environment with access to Genomics and Proteomics core facilities, and a High Performance Computer Cluster. Additional support and resources are also offered through the Office of Postdoctoral
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Engineering, Electrical & Computer Engineering, and Planetary Sciences. We provide direct access to various world-class observational and computational facilities for ground-based radio and optical observing
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Description The Center for Interdisciplinary Data Science and AI (CIDSAI) at NYU Abu Dhabi is a cross‑disciplinary hub where mathematicians, computer scientists, engineers, and social scientists
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fabrication Prototyping Electrical and electronic engineering Numerical simulation Optics Computer-aided design (CAD) Optical and electrical characterization techniques Semiconductor materials # Soft Skills
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successful in this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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to utilize a computer and applicable software to create databases, perform statistical analyses, present data and perform other computer related tasks, such as Microsoft Office. Preferred Special Knowledge
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities