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in our mission to develop clinically useful algorithms, drive high-impact publications, and pave the way for personalised breast cancer treatments. Analyse data from cutting-edge technologies
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in our mission to develop clinically useful algorithms, drive high-impact publications, and pave the way for personalised breast cancer treatments. Analyse data from cutting-edge technologies
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methodologies for quality control in the laser powder bed fusion (LPBF) process. Through the analysis of data from in-Situ AM Process Monitoring tools, machine agnostic algorithms will be generated for quality
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enable the development and operation of new algorithms and software to solve leading-edge research problems. You will find this work exciting if you: Want to help build and maintain some of the largest
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procedure. Your key role within the team will be to develop a robotic swabbing system, conduct user studies for efficacy evaluation, and develop automatic swabbing control algorithms. You will have experience
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considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++). An interest in physiological signals. Home Student
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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algorithms in e.g. MATLAB or PyTorch, and later collaborate with the University of Padova, Italy to deploy controllers to their prototype self-balancing electric bike. Informal enquiries should be directed