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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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geophysical survey and machine learning algorithms. Mainly responsible for processing geophysical/geomechanical data and analysing the data using machine learning. Conducting research, supervising undergraduate
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successful candidate is required to: • Contribute to Robust Splatting, Dialogue, Speech Signal Processing and Machine Learning algorithms. • Contribute to Image Processing and Machine Learning algorithms
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to the implementation and characterisation of AI/ML algorithms for 6G integrated sensing and communications systems, with particular emphasis on embedded platforms, digital processing architectures, efficient
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novel algorithms for efficient, high-fidelity 3D scene reconstruction and representation, including applications in embodied environments where an agent must continuously perceive, update, and reason
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to publications in top-tier international conferences and journals, as well as real-world implementations. The role includes designing novel algorithms, building robust software systems, and collaborating with
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multiple radar units into autonomous vehicle platforms and evaluate perception algorithms in diverse scenarios. The successful candidate will work closely with our industrial partners and will analyse
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support the development, simulation, testing, and implementation of collaborative control algorithms for multi-agent robotic systems, under the guidance of the PI and senior researchers. The work will first
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implement VLA models and world models. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks guided by natural language instructions. Utilize PyTorch to train and fine
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 2 months ago
algorithm performance. You will analyze assignment data from December 2025 to the present to identify workload distribution patterns and refine the algorithm using mathematical modeling, programming, and