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, and machine learning to accelerate and improve the design and simulation of camera lenses. Traditional lens design is slow, labour-intensive, and relies on expert-led iterative methods. We propose
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consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will
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PhD Studentship: Robust, Certified, and Scalable Federated Machine Unlearning for Privacy-Preserving AI About the Project As federated learning systems become increasingly embedded in high‑stakes
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reinforcement machine learning models to enable efficient transpilation of quantum circuits on a wide range of quantum processor modalities and topologies. The key aim of the project is to minimise resource
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(density functional) theory, tight-binding and model Hamiltonians, quantum transport and machine learning, run on national supercomputers (NCI Gadi, Pawsey Setonix) and Monash's NVIDIA Blackwell AI system
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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Mesoscale Modelling, Scientific Software Development and Scientific Machine Learning). You will also participate in a group software development project supported by academics and Research Software Engineers
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, particularly in areas such as machine learning theory, probability theory, optimization, and linear algebra. Experience with random matrix theory is a plus. Programming experience in Python. Familiarity with
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English language requirements . Not have previously completed a PhD. Be able to commence the Program in the year of the offer. The ideal PhD candidate will have: A strong background in machine learning, deep
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal