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Field
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the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication
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optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning
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, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object
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equivalent qualification with one to two years of advanced research experience in generative AI, visual computing and deep learning, and must have no more than five years of post-qualification experience
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, machine learning, and deep learning Good written and oral communication skills Experience in leading research projects Proficiency in basics of programming languages such as Python Self-Motivated and takes
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beyond the PhD in a research or engineering environment focused on large-scale AI. Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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ability to formulate hypothesis and design effective experimental plans. High-level expertise in the required experiment or deep learning methods. Ability to initiate collaboration research in
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and