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efficiency. Investigate parallel algorithms and architectures that can exploit the inherent parallelism in tensor operations. Collaboration: Collaborate with interdisciplinary teams including computer
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Engineering. Solid knowledge of AI/ML, embedded systems, and IoT architecture. Programming skills in Python, ROS, TensorFlow, or MATLAB. Strong interest in applied AI and autonomous systems development
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computing and machine learning. The research will emphasize both theoretical advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more
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learning architectures suitable for deployment on resource-constrained robotic systems. The postdoc will have access to state-of-the-art computational resources. Key Responsibilities: Develop novel methods
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language, vision–language, and vision–language–action models to improve generalization. A key objective is to design lightweight and efficient learning architectures suitable for deployment on resource
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for parallelism in the tensor completion process to enhance computational efficiency. Investigate parallel algorithms and architectures that can exploit the inherent parallelism in tensor operations. Collaboration
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear