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Field
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If you are ready to launch your research career in advanced manufacturing and want to build cutting-edge skills in AI and real-time data-driven production, this PhD opportunity is your gateway. As
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-class honours degree or equivalent) in materials science, manufacturing, mechanical engineering, metallurgy, physics, chemistry, or related fields. Ideal candidates will be self-driven, eager to learn CFD
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format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
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engineering problems is highly desirable. Proficiency in programming languages such as Python, MATLAB, or R. Experience with digital tools for 3D modelling, GIS, or drone-based mapping. Ability to analyse
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engineering teams to implement and test models in production environments What We’re Looking For We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high
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or compromised IoT devices by analysing encrypted traffic patterns, focusing on metadata, flow characteristics, and timing rather than decrypting payloads. The core challenge is creating features and models
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challenge-driven with a systems-based approach and requires interdisciplinary efforts, which is reflected in our team's composition spanning engineering, natural and social sciences. It is a dynamic and
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challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations, and molecular design
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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models. This framework should be engineered to simulate a range of attack scenarios with high fidelity (i.e. exploitation of network and device vulnerabilities). Abertay University possesses a mature, well