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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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. Contribute to scientific publications and disseminate results to decision-makers and communities. Qualifications required PhD in one of the following fields: electronics and embedded systems