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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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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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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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Fraunhofer IGD and the FBN team to safeguard an efficient collaboration and communication between behavioural biologists and computer scientists. The project is part of the KI-Tierwohl project (https://ki
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, computational musicology and music theory, music cognition, spatial/immersive audio, generative music systems, human-computer interaction, machine improvisation, musical robotics, and/or new interfaces
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are in compliance with the necessary trainings (both at the lab and at the institutional level). Minimum Education and Experience: A PhD degree in Computer Science, Electrical/Computer Engineering, or a
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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