The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2021 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 180 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.
Prof Bessant is an expert in developing and applying data science and AI methods to extract novel insights from bioanalytical data, including Raman spectra.
Dr Sapelkin research is in the area of application of advanced light spectroscopy on nanoscale and signal analysis in a variety of solid state and soft matter systems, including cells.
Dr Engl studies how bacteria communicate with, adapt to, and shape their environment using a combination of cellular, molecular, systems, ecology and state-of-the-art imaging techniques.
Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.
This is a multi-disciplinary project at the interface of Bioinformatics, Molecular Cell Biology and Physics. The PhD student will receive training in Machine Learning, Molecular Cell Biology of bacteria, and Raman spectroscopy. This will include one-to-one training sessions, enrolment onto relevant MSc modules and peer-to-peer learning in the lab and at the computer.
Decision-making is an essential hallmark of life. At the cellular level, decision-making is controlled by regulating how much genetic information is converted into physiological output. This process (gene expression) enables cells to determine their fate and shape their surroundings. Gene expression can be surprisingly variable between cells (noise) leading to divergent behaviour of individuals within a group of genetically identical cells; particularly relevant for survival of bacteria which are constantly exposed to fluctuating stressful environments.
Gene expression noise may enable bet-hedging where some cells are pre-adapted to sudden environmental change increasing their survival compared to other un-adapted members of the group. This translates into phenotypic heterogeneity and may increase population fitness in unpredictable environments through risk-spreading among individuals.
By combining our expertise in single-cell Bacteriology, Raman Spectroscopy and Machine Learning we will study divergent behaviours of individual cells within bacterial populations under fluctuating environmental conditions applied with varying dynamics, magnitude and predictability. The data generated will then be used to better understand the origin, extent and consequence of phenotypic noise within bacterial populations.
This studentship is open to students applying for China Scholarship Council funding. Queen Mary University of London has partnered with the China Scholarship Council (CSC) to offer a joint scholarship programme to enable Chinese students to study for a PhD programme at Queen Mary. Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses for 4 years and one return flight ticket to successful applicants.
Applicants must be:- Chinese students with a strong academic background.- Students holding a PR Chinese passport.- Either be resident in China at the time of application or studying overseas.- Students with prior experience of studying overseas (including in the UK) are eligible to apply. Chinese QMUL graduates/Masters’ students are therefore eligible for the scheme.Please refer to the CSC website for full details on eligibility and conditions on the scholarship.
Applicants from outside of the UK are required to provide evidence of their English Language ability. Please see our English Language requirements page for details: https://www.qmul.ac.uk/international-students/englishlanguagerequirements/postgraduateresearch/
Informal enquiries about the project can be sent to Prof Conrad Bessant at c.bessant@qmul.ac.uk
Formal applications must be submitted through our online form by 31st January 2024 for consideration, including a CV, personal statement and qualifications. You must meet the IELTS/ English Language requirements for your course and submit all required documentation (including evidence of English Language) by 14th March 2024. You are therefore strongly advised to sit an approved English Language test as soon as possible.
Shortlisted applicants will be invited for a formal interview by the supervisor. If you are successful in your application, then you will be issued an QMUL Offer Letter, conditional on securing a CSC scholarship along with academic conditions still required to meet our entry requirements. Once applicants have obtained their QMUL Offer Letter, they should then apply to CSC for the scholarship by in March 2024 with the support of the supervisor.
Only applicants who are successful in their application to CSC can be issued an unconditional offer and enrol on our PhD programme. For further information, please go to: https://www.qmul.ac.uk/scholarships/items/china-scholarship-council-scholarships.html
Apply Online
[1] Grimbergen et al. (2015) Microbial bet-hedging: the power of being different. Curr Opin Microbiol 25: 67-72.
[2] Engl (2019) Noise in bacterial gene expression. Biochem Soc Trans 47: 209–217.
[3] Engl et al. (2020) The route to transcription initiation determines the mode of transcriptional bursting in E. coli. Nat Commun 11: 2422.
[4] Kryuchkov et al. (2021) Mean-field model of melting in superheated crystals based on a single experimentally measurable order parameter. Sci Rep 11: 17963.
[5] Sattlecker er al. (2010). Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics. Analyst, 135(5), 895-901.