Research Associate in Machine Learning for Optical Healthcare Technologies
The VISIONLab, led by Prof. Sarah Bohndiek, creates and deploys state-of-the-art spectroscopic imaging tools to improve early cancer detection. We are co-located in the Department of Physics and the Cancer Research UK Cambridge Institute at the University of Cambridge, providing us with a direct pathway from initial testing of brand-new hardware, to application in animal models and deployment in proof-of-principle human clinical trials at Addenbrooke's Hospital, accelerating our healthcare impact.
Applications are invited for a Research Associate to join our collaborative research environment to advance and develop and apply state-of-the-art machine learning analysis for spectroscopy data interpretation and novel biomarker identification. You will drive forward a multidisciplinary project at the intersection of biophysical modelling and machine learning. You can make use of the large library of pre-existing datasets from our laboratory to test novel data analysis strategies or engage in current projects to tailor new datasets. You will take a key role in a major EPSRC-funded initiative, the Ubiquitous Optical Healthcare Technologies (UbOHTs) Programme Grant, which focuses on developing the next-generation of biosensors for precision health and is a collaboration between the Universities of Cambridge, York, Strathclyde and Exeter. Involvement in the programme grant will provide opportunities for training, networking and developing leadership skills on a national and international scale.
We are looking for a highly motivated and curiosity-driven individual with a PhD in a topic related to computer science, mathematics, physics, or biomedical optics. Demonstrable experience in successful delivery of computational projects and a high level of confidence in Python programming is essential. Practical knowledge of data-handling from at least one hyperspectral imaging technology, such as diffuse reflectance spectroscopy, photoacoustic imaging, or Raman micro-spectroscopy is desirable. Experience in Monte Carlo modelling of light-tissue interactions, an understanding of computational image reconstruction, or experience in biomedical image analysis would also be desirable. It is essential that you demonstrate a strong open-science ethos.
You should be able to demonstrate a strong scientific track record including paper, data, and open-source code publication, as well as presentation at international research meetings, commensurate with your research experience. Given the interdisciplinary nature of the laboratory, excellent communication and team working skills are essential.
Key responsibilities will likely include: advanced computational modelling and simulation; biomarker identification and validation from existing datasets; participating in experimental studies to enable computational work; and supervision of graduate and undergraduate students in the laboratory.
Appointment at research associate level is dependent on having a PhD, including those who have submitted but not yet received their PhD, in which case appointment will initially be made at research assistant and amended to research associate when the PhD is awarded.
The VISIONLAB strongly encourages and supports candidate-driven efforts to obtain competitive fellowships.
For more information on the research of the laboratory, please see http://www.bohndieklab.org
The successful candidate would ideally start as soon as possible after receiving the offer. Support is provided towards relocation expenses where appropriate.
Fixed-term: The funds for this post are available for 2 years in the first instance.
Once an offer of employment has been accepted, the successful candidate will be required to undergo a basic disclosure (criminal records check) check, a health assessment and a security check. This appointment also requires a Research Passport application.
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Please email molly.bridger@cruk.cam.ac.uk with informal enquiries or specific questions about the role.
Please quote reference KA43104 on your application and in any correspondence about this vacancy.
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