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Biomedical Engineering New York University Biomechatronics and Intelligent Robotics Lab led by Prof. Hao Su (http://haosu-robotics.github.io) at NYU Tandon School of Engineering is seeking to hire a Part Time
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developing and benchmarking planning algorithms, and as a system-level validation tool before real-world deployment. By the end of the contract, the postdoctoral researcher is expected to play a leading role
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
for the following main tasks. They will engage in Model Design and Development by designing and implementing novel architectures (e.g., Diffusion Models, Transformers, VAEs) specifically tailored for high-resolution
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will include development of algorithms for heterogeneous computing architectures and implementation of AI/ML in a real-time environment. The candidate will also have the opportunity to conduct
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constraints. The postdoc will work at the interface of reinforcement learning and computational epidemiology, focusing on the development of new reinforcement learning algorithms. The project will consider
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investigate the influence of trajectory accuracy on player perception and engagement within a dedicated laboratory environment. Developing distributed synchronization algorithms: we will design and implement a
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INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA | Portugal | 3 months ago
information is available at: https://www.dges.gov.pt/pt/pagina/reconhecimento?plid=374 . Work plan and objectives to be achieved: The objective of this project is to study and develop advanced methodologies
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Fellow to conduct cutting-edge research in high-resolution electromagnetic imaging and intelligent sensing. The successful candidate will contribute to the theoretical development, algorithm design, and
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reactiOns) algorithm, developed within the DeSIS research team, which combines experimental measurements and simulated data to improve predictions of secondary particle production cross sections. High-quality
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a