Fully funded BBSRC PhD's in the area of artificial intelligence and Data-Driving Economy.

Thanks to a grant from the NPIF and the BBSRC the University of Cambridge is able to offer four fully funded PhD’s in the area of artificial intelligence and Data-Driving Economy.

Successful candidates must be able to start their PhD before 30 December 2018.

Candidates are asked to select a project from the list below and apply to the corresponding department by the 30th June, midday.

This is a fully funded PhD with a stipend.

Funding rules of BBSRC stipulate that applicants must be UK citizens to receive the full award.

Project Descriptions

Application of artificial intelligence methods for understanding genome regulation

This PhD project will develop and apply machine learning artificial intelligence methods for the simultaneous analyses of different types of high-throughput sequencing data to extract biologically meaningful patterns and associations between chromatin factors and to determine principles of genome control.

Prof. Ahringer, Gurdon Institute; Dr Meeds, Microsoft Research

Apply: PhD in Genetics

Using machine learning to distinguish between different forms of autophagy

This project will develop methods to distinguish visual representations of autophagy from related processes using convolutional neural networks.

Dr Beale, Dept of Pathology; Dr Ktistakis, Babraham Institute
Dr Johnson, Microsoft Research

Apply: PhD in Pathology

Deep learning of disease-vector biology: hacking the mechanism of mosquito adaptation and pathogenic immune evasion

This project will apply deep learning approaches to mosquito genome annotation to help understand the interplay between vector adaptation and pathogen immune evasion. The outcome of the computational analysis will be validated experimentally using structural, molecular and cellular biology techniques.

Dr. Gangloff, Prof Gay, Dept of Biochemistry;
Dr. Ward, Prof. Hain Fetch.ai

Apply: Biochemistry

Domestication of the Black Soldier Fly

The Black Soldier Fly is used to degrade food waste and produce high quality animal feed, and this project will study its genome to understand how the species has adapted to life as a domesticated insect.

Prof Jiggins, Dept of Zoology, Prof Richard Durbin, Dept of Genetics
Miha Pipan, Entomics

Apply: Zoology

Machine learning to improve life sciences metadata collection and data reuse

AI and ML techniques have great potential but their broad application is hampered by heterogeneous data. This project will tackle this bottleneck from a variety of angles and has the potential for substantial impact. Students with strong mathematical and computational skills will be at a great advantage.

Gos Micklen, DAMTP; Pietro Liò, Computer Science & Technology
Dr Nick Brown, AstraZeneca

Apply: DAMTP

A machine intelligence pipeline for single-cell characterisation of genetic devices

This project will use machine learning and AI techniques to predict the population level behaviour of gene circuits from the parameters of gene circuit components inferred from single cell time-lapse microscopy data.

Dr. Locke, Sainsbury Laboratory
Dr. Phillips, Microsoft Research

Apply: Physics

Personal genomics of sports health and fitness

Combining genetic data with extensive tracking data from elite athletes, both for scientific discovery and for improving interactive genetically-adjusted training programs dynamically set by a machine-learned coach using feedback from tracking devices.

Prof. Gough, MRC LMB
Genetrainer Limited

Apply: MRC LMB