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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 7 hours ago
, and C/C++ Experience developing machine learning algorithms Strong mathematical foundation specifically in linear algebra, differential equations, and optimization Solid communication and teamwork
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validate advanced 5G features such as network slicing, MEC and xApp/rApp. Contribute to the development of innovative solutions and algorithms to enhance 5G network capabilities. Work closely with
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on wind turbines and ships. This involves advanced computer vision, dedicated lighting systems, optical communication, and robust control and guidance algorithms. You will work on: Design and implement
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the implementation and testing of algorithms. * Strong programming skills in R or Python. * Familiarity with data science and visualization libraries in R or Python. * Experience with GitHub, conda environment and
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, TensorFlow) and Python ML libraries (e.g., NumPy, OpenCV, scikit-learn). Experience implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based
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. The projects will also focus on applications and algorithms employing X-ray scattering models to uncover spatiotemporal correlations in ultrafast phenomena, particularly fluid dynamics, studied at the recently
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performance. Duties and responsibilities include the development of new abstractions and algorithms using computational sheaf theory. Such algorithms will be developed for understanding composite energy-driven
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implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based approaches. Proven ability to design and run rigorous experimental frameworks, including
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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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this short-term project we shall use active learning to accelerate the training of deep learning algorithms for optimising 2D material van der Waals (vdW) structure discovery. The goal is to make model