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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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and plasticity of circadian clock neurons in rodents and fruitflies. Applicants must have a PhD in Biology or Neuroscience and experience in molecular biology, electrophysiology and animal handling, as
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the beginning of their appointment, have demonstrated teaching effectiveness as an instructor or teaching assistant, and have expertise to teach foundational AI courses such as introduction to AI, machine
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PhD student will expect to develop some experience in developing power systems models using a range of computer languages and tools (e.g. Python, MATLAB, OPNET, etc), ideally for applications involving
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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which must be advanced courses in computer science, mathematics, AI, machine learning or similar. The applicant is expected to have good knowledge of computer science, mathematics, algorithms, and
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applications for faculty positions in Computer Science. Faculty specialising in data science, machine learning (deep learning, reinforcement learning, multimodal learning), Generative AI, and computer graphics
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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and innovative learning initiatives. Senior Assistant Professor A PhD or equivalent doctoral qualification in a relevant discipline. Demonstrated track record of research excellence and sustainable peer