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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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machine learning conferences/journals Job Requirements: PhD in Mathematics and/or Electronics and/or Computer Science Ability to work independently and as part of a team with strong initiatives Good
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. Qualifications/Requirements Qualifications / Discipline: - PhD from a reputable institution in Physics, Bio-imaging, Computer Science, or a scientific domain closely related to Machine Learning. - The candidate
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storage materials, and employing machine learning and high throughput for the discovery of new electrode materials and electrolyte systems. 1. Holds a PhD degree in chemical engineering, chemistry
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storage materials, and employing machine learning and high throughput for the discovery of new electrode materials and electrolyte systems. 1. Holds a PhD degree in chemical engineering, chemistry
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storage materials, and employing machine learning and high throughput for the discovery of new electrode materials and electrolyte systems. 1. Holds a PhD degree in chemical engineering, chemistry
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, Safety and Health Regulations. • Assist in project resource requirements and managing student projects. Job Requirements: • Have a PhD Degree in Computer Engineering/Computer Science/Electrical Engineering
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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. Perform any other duties relevant to the research programme. Job Requirements: PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive