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Field
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applies. 2 Fixed-term position available for up to 24 months. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning -- the largest
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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning
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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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(CBmE) was established in September 2006 in the Yong Loo Lin School of Medicine through a generous gift by the Chen Su Lan Trust. CBmE, directed by Prof Julian Savulescu, is a thriving centre for learning
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bone marrow transplantation. We are looking for a highly motivated candidate with a PhD degree interested in using microbiological methods with the aim of developing novel strategies to improve bone
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the system Development of inverse design frameworks using machine learning Development of full simulation for the chip-scale chirped-pulse amplification Use the full simulation to guide system fabrication
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the High Performance Computing and Data Center, to be completed on campus by August 2026. This position collaborates with others in the growing machine learning and exoplanet subgroups within the Physics and
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at the Institute of Genetics and Cancer. Informal enquiries may be directed to Dr Athina Spiliopoulou (A.Spiliopoulou@ed.ac.uk ). Your skills and attributes for success: PhD in machine learning, genetic epidemiology
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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