PhD Studentship: Machine Learning-Guided Engineering of Next-Generation Cancer Therapeutics

Overview

The University of East Anglia, through the School of Chemistry, Pharmacy, and Pharmacology, offers an interdisciplinary PhD studentship focused on machine learning-guided engineering of next-generation cancer therapeutics. The primary supervisor for the project is Dr Amit Sachdeva. The research takes place on site in the United Kingdom as a full-time, fixed-term position starting on 1 October 2027.

The project addresses the limitation of conventional full-length antibodies, which often struggle to penetrate solid tumours deeply due to their large size. The student will use advanced machine learning methods to design small, highly effective antibody fragments. These computationally engineered molecules will be expressed in Escherichia coli, followed by cloning, expression, purification, and biochemical and biophysical characterisation. Binding affinity will be assessed on cancer cell lines, and promising candidates will incorporate non-canonical amino acids via synthetic biology to enhance therapeutic performance.

Who can apply

This opportunity is open to UK applicants who are competing for a Faculty of Science funded studentship. Candidates should be highly motivated students in biochemistry, chemistry, molecular biology, biotechnology, or related disciplines.

Requirements

Applicants must hold a minimum entry requirement of a 2:1 degree in Biological Sciences, Biochemistry, Chemical Biology, Chemistry, or related fields.

What you get

Successful funding comprises home tuition fees and an annual stipend for three years. The current institutional PhD stipend rate is 21,805 British pounds per annum, which is subject to an agreed inflationary uplift for the 2027 to 2028 period. The candidate will also receive training in machine learning-guided protein design, molecular cloning, recombinant protein expression, protein purification, cell-based assays, gel electrophoresis, Western blotting, and chemical protein modification techniques.

Key dates

The application closing date is 24 February 2027. The mode of study is full-time, with the start date scheduled for 1 October 2027.