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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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Open PhD position: Waste to Medicine Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 36-month funded PhD
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expertise in MRI, biomaterial characterization, computer vision, and machine learning with applications in biology, food science, and oral health. The research leverages cutting-edge resources from
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PhD Studentship: A continual learning approach for the development of robust robotic control systems
. AI is expected to be at the centre of these systems, being the foundation of computer vision, monitoring, and control solutions. Despite the promising results that AI (and especially Deep Learning) has
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photonic design software (Lumerical, Comsol, MEEP or HFSS) will be an advantage. A solid understanding of electromagnetics, mathematics and statistics, and machine learning theory/algorithms, with excellent
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groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional Programming Intelligent Modelling and Analysis
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field. Alternatively, candidates with an MRes or MSc in a relevant field are encouraged to apply. Candidates should demonstrate strong analytical skills and a keen interest in satellite systems or machine
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robotics, and materials science. Project description: 3D-printing of soft robotics is a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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and challenging materials. Artificial Intelligence and Machine Learning techniques will be employed to analyse experimental data, enabling deeper insights and faster optimisation of the nozzle design