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

Primary Supervisor - Dr Amit Sachdeva Monoclonal antibodies have transformed the treatment of cancer by selectively recognising tumour-associated receptors and triggering cellular responses that lead to cancer cell death. Despite their clinical success, conventional full-length antibodies often show limited effectiveness against solid tumours because their large molecular size restricts deep penetration into tumour tissue. Overcoming this challenge represents a major opportunity in the development of next genera...

Opportunity details

Primary Supervisor - Dr Amit Sachdeva Monoclonal antibodies have transformed the treatment of cancer by selectively recognising tumour-associated receptors and triggering cellular responses that lead to cancer cell death. Despite their clinical success, conventional full-length antibodies often show limited effectiveness against solid tumours because their large molecular size restricts deep penetration into tumour tissue. Overcoming this challenge represents a major opportunity in the development of next generation cancer therapeutics. This interdisciplinary PhD project aims to develop small, highly effective antibody fragments capable of penetrating solid tumours more efficiently than conventional antibodies. The student will employ state-of-the-art machine learning approaches to design novel antibody fragments. These computationally engineered molecules will then be expressed in Escherichia coli. The project will involve cloning, expression and purification of engineered antibody fragments, followed by detailed biochemical and biophysical characterisation. Their binding affinity to the target will be evaluated on cancer cell lines, using our previously developed methods. Furthermore, the most promising candidates will be equipped with non-canonical amino acids through synthetic biology approaches, enabling the introduction of new chemical functionalities to improve targeting, and therapeutic performance. Working at the interface of chemistry, biology, and artificial intelligence, the successful candidate will gain extensive training in cutting-edge methodologies including machine learning-guided protein design, molecular cloning, recombinant protein expression, protein purification, cell-based assays, gel electrophoresis, Western blotting and chemical protein modi...

Eligibility

The standard minimum entry requirement is 2:1 in Biological Sciences, Biochemistry, Chemical Biology, Chemistry, and related fields. Mode of Study Full-time Start Date 1 October 2027 Additional Funding Information This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.

Requirements

The standard minimum entry requirement is 2:1 in Biological Sciences, Biochemistry, Chemical Biology, Chemistry, and related fields. Mode of Study Full-time Start Date 1 October 2027 Additional Funding Information This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.

Funding and benefits

Funding eligibility: UK Students. Funding amount: £21,805.

How to apply

This PhD is ideally suited to a highly motivated student in biochemistry, chemistry, molecular biology, biotechnology, or a related discipline who is excited by the prospect of applying computational and experimental approaches to address pressing challenges in cancer therapy.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.