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(UCL). The successful candidate will join the internationally renowned Biomedical Optics Research Laboratory (BORL) and work within the research programme led by Professor Ilias Tachtsidis, who leads
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and evaluation of the developed methods using relevant imaging datasets and downstream computer vision tasks. This appointment is subject to UCL Terms and Conditions of Service for Research and
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apply them to cutting-edge single-cell datasets generated within the lab and from public resources. There is significant scope to shape the project around your interests, including live-cell imaging, flow
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queries about the application process, please contact the FLS HR team on [email protected] . About you You must have a PhD or equivalent in primate or human evolutionary anatomy or a related subject
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About us UCL’s Department of Computer Science is a top-ranked Computer Science Department in the UK. In the 2021 Research Excellence Framework evaluation, UCL Computer Science was ranked second in
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Improving the clinical success of porous orthopaedic designs – experimental and finite element study
organisational skills, and the ability to work as a team player. Proficiency in Finite Element Analysis software (e.g., Abaqus, Ansys), image processing techniques, and programming languages such as Python and
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to: Develop AI and machine learning workflows to analyse microscopy and high-content imaging data from advanced in vitro models. Build computer vision pipelines for image segmentation, tracking, phenotypic
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These include but are not limited to: Developing and validating organoid, spheroid and complex in vitro model workflows for the VISIBLE platform. Designing and executing advanced cell culture, imaging and
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evaluating computational methods, analysing imaging data, collaborating with clinicians for real-world impact, and contributing to publications. About You PhD (or near completion) in computer science
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perturbation, live and fixed imaging, and mechanical measurement or perturbation as the questions require; Analysing, interpreting, and integrating the resulting data with the lab’s existing multiomic datasets