PhD Studentship: Scalable Bayesian Nonparametric Models for Biomedical image Analysis @University of Sussex, UK and A*STAR

PhD Studentship: Scalable Bayesian Nonparametric Models for Biomedical image Analysis
University of Sussex, UK and A*STAR, Singapore
Funding for: EU and International Students
Funding duration: 4 years
Funding amount: A stipend at Sussex at the standard Research Council rate (currently £14,553 per annum) plus UK/EU tuition fees waiver and a full stipend directly from A*STAR for years 3 and 4 (currently SGD$2500 per month).
Deadline: Applications accepted until position is filled.
Overview
Applications are invited for an exciting 4-year PhD studentship to research on developing probabilistic nonparametric learning methods suited for analysing biomedical images including but not limited to cellular images. The first challenge is to have expressive nonparametric models that can capture structural pattern of cells from microscopic images, and the second challenge is to have scalable methods that can update models to handle newly acquired biomedical images as time evolves. Biomedical images such as cellular image analysis has become crucial in many areas of biological studies, partly due to the rapid progress of 3D/super-resolved/time-lapse microscopic instruments that open doors to many new opportunities. Meanwhile, the current practice in biomedical field largely relies on manual annotation by specialists, which is often tedious and susceptible to subjective inconsistencies.
The project provides a unique opportunity to work at the interface between cutting-edge research in nonparametric probabilistic methods and biomedical image analysis. University of Sussex is a research intensive university, in the sunniest part of UK, 50min by train to Central London, and 30min by cycle to the Brighton Beach (which has a 300m long zip wire). Bioinformatics Institute, A*STAR, Singapore is located in a research hub with a number of renowned research institutions, in the beautiful island nation of Singapore.
Applications are invited from individuals with a 1st class or high upper 2nd class Bachelor's or Master's degree with distinction or merit in Computer Science, Statistics, Mathematics or Physics, and an interest in statistical machine learning, Gaussian processes, computer vision, and image analysis. Strong programming skills in at least one of C/C++/R/Python/Matlab are essential.
Procedure
Applications must be submitted via the University of Sussex Postgraduate Admissions System at https://www.sussex.ac.uk/pgapplication/. Please select Informatics PhD as your programme of study and indicate Dr. Novi Quadrianto as your preferred supervisor.
Informal enquiries about the position are welcome and may be addressed to Dr. Novi Quadrianto (http://www.sussex.ac.uk/profiles/335583) or Dr. Li Cheng (https://web.bii.a-star.edu.sg/~chengli/).