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Field
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to conduct research, solve complex technical problems, and develop innovative solutions. Experience with parallel systems, machine learning/AI, or performance benchmarking is highly desirable. Ability to work
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The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum
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Requirements: Candidates should hold a PhD in civil engineering, computer science, electrical/computer engineering, robotics, or a related field. Experience in computer vision, deep learning, 3D reconstruction
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, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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Job Summary: The Research Fellow will be responsible for undertaking in-depth research and innovation in machine learning, data science, and artificial intelligence on cybersecurity that leads
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Engineering or equivalent. Strong background in machine learning and computer vision. Prior experience in 3D vision is preferable. Strong publication records in top-tier machine learning or computer vision
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(for Research Engineer) with expertise in Computer Science, Artificial Intelligence, Computer Vision, Machine Learning, Robotics, or a closely related field. Experience in developing, training, validating and
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and student guidance. Assist in the preparation and writing of research grant proposals. Job Requirements: PhD degree in computer engineering, Computer Science, Electronics Engineering or equivalent
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep