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Field
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foundation models for general manipulation based on vision-language-action models Develop learning from human demonstration (LfD) framework for vision-language-action models Develop multi-embodiment
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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) Application Deadline 2 May 2026 - 21:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not
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expected to commence the post between August 2026 and September 2027 and will register for a PhD programme. PhD fees and research consumables are funded by the NIHR HPRFA award. For resident doctors in GMC
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learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image analysis, that is medical images that evolve over
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Language Models (MLLMs) and their agentic implementations. Develop and benchmark novel adversarial attacks and defense strategies, focusing on the intersection of computer vision, natural language processing
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research grants in the above areas Job Requirements: A PhD degree in Computer Science, Data Science, Engineering, or a related field. Research experience in Computer Vision, Image Processing, Multimedia
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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. The PhD position is for a fixed term, with the objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related