In the next 10 years space telescopes such as Ariel and ground-based instruments such as ELT will begin detailed surveys of known exo-planets, including looking for the signatures of life. With a vast number of potential targets, measurements being extremely time and resource intensive, and most exo-planetary systems being very different to our own solar system, we need a consistent method for selecting only the most promising candidates. In collaboration with the Astronomy Unit (AU) at QMUL, this project will combine biophysical modelling with machine learning techniques, such as evolutionary algorithms and reinforcement learning, to provide a more sophisticated definition of ‘potentially habitable’.
Our basic assumption is that life, however it may evolve, requires energy, with the most likely source being the parent star. We therefore aim to understand how photosynthetic processes would evolve in different environments. Although biological structures are extremely diverse, their evolution is constrained by simple and universal physical laws. The student on this project will model how a photosynthetic ‘surface’ (such as that found within chloroplast and photosynthetic bacteria) evolves according to evolutionary pressure to maximize energy conversion, conserve resources, and minimize damage. In collaboration with the AU, they will then model how such photosynthetic systems would interact with planetary surfaces and atmospheres. The ultimate goal is to then predict (1) which star-planet systems should be priority targets and (2) determine what biosignatures we should be looking for when analysing observational data.
The Duffy Lab is based in the Digital Environment Research Institute (DERI) at QMUL, a cutting machine learning and digital technology research institute in London. The lab in composed of several PhD students and a PDRA working on various aspects of photosynthesis, metabolic engineering and astrobiology. The lab works in close collaboration with the Astronomy Unit and the Photosynthesis Group at QMUL, and is part of the London Exo-Planet Network. The applicant with be embedded within DERI and will receive training in theoretical biophysics and machine learning, and will be encouraged to be an active member of the Exo-Planet Network.
Find out more about the School of Biological and Behavioural Sciences on our website.
We are looking for candidates to have or expecting to receive a first or upper-second class honours degree and a Master’s degree in an area relevant to the project such as Physics, Chemistry, Computational Biology, Astrophysics, Biophysics
Knowledge of Techniques in Machine Learning and/or Thermodynamics and Statistical Mechanics would be highly advantageous but are not required.
You must meet the IELTS requirements for your course and upload evidence before CSC’s application deadline, ideally by 1st March 2025. You are therefore strongly advised to sit an approved English Language test as soon as possible, where your IELTS test must still be valid when you enrol for the programme.
Please find further details on our English Language requirements page.
Formal applications must be submitted through our online form by 29th January 2025 for consideration. Please identify yourself as a ‘CSC Scholar’ in the funding section of the application.
Applicants are required to submit the following documents:
Find out more about our application process on our SBBS website.
Informal enquiries about the project can be sent to Dr Chris Duffy AT c.duffy@qmul.ac.uk Admissions-related queries can be sent to sbbs-pgadmissions@qmul.ac.uk
Shortlisted applicants will be invited for a formal interview by the supervisor. If you are successful in your QMUL 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 with the support of the supervisor.
For further information, please go to the QMUL China Scholarship Council webpage.
Apply Online