PhD Projects in Generative Biology

Method Development for Engineering Biology - Open to Student-Led Project Proposals

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Project Summary

For research with the Generative Biology Institute (GBI), students are encouraged to propose their own projects as part of their application, which can then be developed in collaboration with their supervisor(s). Areas of interest which align with the GBI’s vision and research aims are listed below.

The vision of the GBI is to lay the foundations for engineering biology and unlock its potential for good. To achieve this, we must overcome two key challenges. First, we need the ability to write in the natural language of biology, enabling the rapid and scalable synthesis of entire genomes with precision. Second, we must understand what to write in DNA, determining which DNA sequences will generate biological systems that perform the desired functions. Addressing these challenges will allow us to harness the full power of biology to create transformative solutions across health, agriculture, clean energy and more. GBI is focused on solving the two critical challenges in making biology engineerable and applying the solutions to addressing the global challenges encapsulated in EIT’s humane endeavours.

Areas of interest

We welcome and encourage original research proposals that are consistent with GBI’s vision. Some relevant areas include:

  • Molecular and Cellular Design and Evolution.
  • Enzyme design.
  • Design of molecular assemblies and machines.
  • Experimental accelerated evolution.
  • Robotics, automation and autonomous labs.
  • Genome mining and informatics-based discovery.
  • Computational AI sequence to function.
  • Expanding chemistry in biology.
  • Scalable error free DNA and genome synthesis.
  • Microbial Genome Synthesis and Design.
  • Gb-scale Genome Synthesis for plants, human cells and animals.
  • Combinatorial synthetic genomics.
  • DNA delivery.
  • Predictive models of DNA sequence to function, at the scale of genes and genomes.
  • Human health applications and delivery mechanisms.

Potential Supervisors  

  • Professor Jason Chin (Founding Director, GBI, EIT & Professor of Chemistry and Chemical Biology, Department of Chemistry, University of Oxford)  
  • Dr Jérôme Zürcher (Group Leader, GBI, EIT)
  • Dr Rongzhen Tian (Group Leader, GBI, EIT)

Skills Recommended

  • A Master’s Degree (or equivalent) in a relevant scientific discipline (e.g. Biology, Chemistry, Engineering, Computer Science)
  • Experience of hands-on research in a laboratory setting
  • Proven ability to work independently, think creatively, and solve complex problems
  • Experience with data analysis, automation platforms, or computational tools relevant to the field
  • Experience preparing publications and delivering scientific presentations
  • Strong organisational skills and the ability to manage multiple parallel workstreams
  • Excellent written and verbal communication skills, including the ability to collaborate across multidisciplinary teams
  • A proactive mindset and enthusiasm for working in a fast-paced, high-growth research environment

University DPhil Courses 

Supervisors

We are bringing together experts from across the globe, with a shared drive to create lasting impact.

VP of AI Research & Principal Scientist

Dr Danilo Jimenez Rezende

VP of AI Research for AI & Robotics at EIT. Former Director at Google DeepMind.

VP of Applied Machine Learning

Dr Matej Macak

VP of Applied Machine Learning for AI & Robotics at EIT. Chief Technologist at Every Cure.

Principal Scientist & Executive Vice President - AI & Robotics Institute

Professor Cecilia Lindgren

Co-Director of the AI & Robotics Insitute at EIT, Professor of Genomics of Endocrinology and Metabolism at the University of Oxford, Director of the Oxford Big Data Institute.

Executive Director & Principal Scientist - AI & Robotics Institute

Professor Chris Holmes

Co-Director of AI & Robotics Institute at EIT. Professor of Biostatistics at the University of Oxford. Researching theory, methods and applications of statistics and statistical modelling.