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Field
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strong international track record in content provenance and authenticity research spanning computer vision, watermarking, machine learning, privacy-preserving technologies and open standards. We have
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning, Assessing
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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biophysics and machine learning for the accurate prediction and design of functional enzymes; and explore their real-world applications. Job Requirements Ph.D. in a relevant area, e.g., Chemical Engineering
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, epidemiology, biostatistics, machine learning or a closely related quantitative discipline. Strong knowledge of analytical methods relevant to epidemiological and biomedical research, including machine-learning
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Engineering, Computer Engineering, HealthTech, MedTech, Product Development, Clinical Engineering or related disciplines with relevant experience in engineering, prototyping, product development or healthcare
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Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and