University of Bristol (UoB)

Research Associate / Computational Postdoc

University of Bristol (UoB)

Bristol, United Kingdom

Research Associate
Job number ACAD104047
Division/School – School of Cellular and Molecular Medicine
Contract type – Open Ended
Working pattern – Full time
Salary £33,199 – £37,345 per annum
Closing date for applications 31-Aug-2019

A postdoc position in computational image analysis and AI is available for a highly motivated and talented researcher with expertise in quantitative image analysis (particularly in cell segmentation/tracking from multi-channel time-lapse fluorescence microscopy images) and machine learning/artificial intelligence (ML/AI; particularly novel approaches like CNNs, GANs) in the group of Prof Rafael Carazo Salas in the University of Bristol UK (https://research-information.bristol.ac.uk/en/persons/rafael-e-carazo-salas(a7638b29-53e4-49ba-82b5-98b21d82f41f).html).

The goal of this project is to clarify and predict how individual human pluripotent stem cells (hPSCs) make cell fate decisions using computational image analysis and ML/AI, by mining tens of thousands of cell lineages and millions of single-cell data points in feature space that our group routinely generates by multiday/multicolour time-lapse epifluorescence microscopy.

Applicants should hold a PhD in Computational Image Processing, Computer Vision, Machine Learning/Artificial Intelligence or a related subject.

The project is part of an international HFSP collaboration with partners in Switzerland and the USA.

The vacancy will be open until the position is filled with shortlisting starting from 10 July 2019.

For informal enquiries please contact Professor Rafael E. Carazo Salas, phone: +44 (0)117 331 2046 or email: rafael.carazosalas@bristol.ac.uk.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.

http://www.bristol.ac.uk/jobs/find/details.html?nPostingId=45194&nPostingTargetId=144174&id=Q50FK026203F3VBQBV7V77V83&LG=UK&mask=uobext

Please apply via recruiter’s website.

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