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Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi Project description: The University of Southampton
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our high-fidelity multidisciplinary design optimisation (MDO) framework for the design of BLI fans. The proposed MDO is based on adjoint aerostructural optimisation, augmented with machine learning
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privacy-preserving technology in Machine Learning. Despite its advancements, FL systems are not immune to privacy breaches due to the inherent memorisation capabilities of deep learning models
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and collaborate with a strong multidisciplinary team of academics at University of Southampton and experts from University College London. Person specifications: Master degree in machine learning
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or electronic engineering, machine learning, medical physics or neuroscience. Knowledge of electronics is essential. Strong programming skills (MATLAB is essential) & Phyton (TensorFlow library, desirable
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(notably through neural networks and machine learning) is now emerging as a means both to implement and to optimise the control. The Smart Fibre Optics High-Power Photonics (HiPPo) programme is a new £6
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Supervisory Team: Hector Calvo-Pardo; Vahid Yazdanpanah; Tiago Alves (Solar Americas ); Enrico Gerding PhD Supervisor: Hector Calvo-Pardo Project description: Machine learning (ML) holds immense
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PhD Supervisor: Prof Thomas Blumensath Supervisory Team: Prof Thomas Blumensath Project description: The University of Southampton is expanding its PhD research in the area of Quantum Technology
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' diseased hearts. These models, coupled with machine learning techniques, contribute to the identification of crucial mechanistic relationships and features that offer insights into the trajectory of a
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PhD Supervisors: Dr Peter Horak, Dr James Gates Supervisory Team: Dr Peter Horak, Dr James Gates Project description: The University of Southampton is expanding its PhD research in the area of