Antibodies and therapeutic proteins have transformed modern medicine. They treat cancer, autoimmune disorders, infectious diseases, and more with remarkable precision. Yet traditional discovery methods remain slow, expensive, and limited in their ability to explore the vast space of possible molecular designs.
What if artificial intelligence could change that? What if you could help build the AI systems that design safer, more effective, and easier-to-manufacture antibodies and proteins from the ground up?
Imperial College London is offering a fully funded PhD studentship in exactly this space. Based in the Department of Chemical Engineering, the project focuses on developing innovative AI methods to design therapeutic-grade antibody and protein sequences. It represents a rare opportunity to work at the cutting edge of machine learning, computational biology, and real-world therapeutic impact.
Scholarship Summary
- Host Country: UK
- Host University: Imperial College London
- Scholarship Type: PhD Scholarships
- Eligible Countries: All Countries
- Scholarship Benefits: Full tuition fee, Living stipend, etc.
Why AI for Antibody and Protein Design Matters Now
The global market for monoclonal antibodies already exceeds hundreds of billions of dollars, and the AI in antibody discovery segment is growing at over 22% CAGR. Pharmaceutical companies and biotech firms are racing to adopt computational approaches because traditional library screening and immunization campaigns take years and cost tens of millions per successful candidate.
Recent advances in foundation models, diffusion models, and geometric deep learning have shown that AI can generate novel protein sequences with desired properties. However, antibodies present unique challenges: they must bind targets tightly while remaining stable, soluble, low-immunogenicity, and manufacturable at scale. Balancing these competing objectives requires sophisticated multi-objective optimisation and tight integration between computational design and experimental validation.
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This PhD project sits at the heart of that challenge. You will develop and evaluate machine learning methods that generate antibody and protein sequences with improved specificity, stability, solubility, and reduced immunogenicity. The work may draw on repertoire-scale foundation models, geometrical deep learning, diffusion models, and active learning frameworks, all connected to experimental feedback from protein biophysics assays in the lab.
The outcomes are expected to be open, usable tools that the broader research community and industry partners can adopt for real design problems.
About the Fully Funded PhD Studentship
This is a single-award, fully funded studentship for the 2026/2027 academic year. It covers tuition fees for both Home and Overseas students and provides a competitive stipend to support living costs. The position is full-time and based in the Department of Chemical Engineering at Imperial College London.
The start date is as soon as possible, making it ideal for candidates ready to begin impactful research without waiting for the traditional October intake. Because it is open to new entrants only, current Imperial students are not eligible.
Research themes include:
- Foundation models for proteins and antibodies
- Multi-objective optimisation
- Active learning
- Interpretable machine learning
- Sequence-structure integration
You will have access to experimental datasets, modern ML frameworks, and a collaborative interdisciplinary environment with strong mentoring and opportunities to publish both tools and datasets.
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- The Canon Foundation Japan Africa Exchange Scholarship 2027 – Your Gateway to Transformative Research and Cross-Continental Collaboration
Who Should Apply for the Fully Funded PhD AI Antibody Protein Design At Imperial College London? Ideal Candidate Profile
The ideal candidate will have a strong interest in coding (particularly Python), quantitative thinking, and applying machine learning to biological problems. Prior hands-on experience in machine learning is beneficial but not essential. What matters most is genuine motivation to work at the interface of computation and therapeutic design, combined with the ability to think rigorously and learn quickly.
Strong quantitative backgrounds from chemical engineering, computer science, bioinformatics, physics, mathematics, or related fields are highly suitable. If you enjoy writing clean code, thinking about optimisation problems, and are curious about how AI can accelerate the creation of life-saving medicines, this project could be an excellent fit.
The Supervisor and Research Environment
The project is led by Professor Pietro Sormanni, a recognised leader in computational protein and antibody design. His research sits at the interface between computation and experiment, developing data-driven technologies that use AI and biophysical principles to design better antibodies and proteins.
Professor Sormanni previously held a Royal Society University Research Fellowship at the University of Cambridge and has built significant expertise in deep learning approaches for antibody nativeness, pairing likelihood, and developability. At Imperial, his lab continues this mission with a strong emphasis on creating practical, open tools that can be used by the wider scientific community and industry partners.
Joining this group means working in a supportive, interdisciplinary setting where computational methods are directly informed by experimental results from protein biophysics assays. You will have scope to shape the methodological focus according to your interests — whether that leans more toward generative models, active learning loops, interpretable AI, or tighter sequence-structure integration.
Why Imperial College London?
Imperial College London consistently ranks among the world’s top universities. In the QS World University Rankings 2026 it placed 2nd globally, while the Times Higher Education World University Rankings 2026 placed it 8th. It excels particularly in engineering, computer science, life sciences, and medicine — the exact disciplines this PhD bridges.
Located in the heart of London, Imperial offers unparalleled access to academic collaborators, biotech and pharmaceutical industry partners, and a vibrant research ecosystem. The Department of Chemical Engineering itself is world-leading and provides an ideal home for research that combines molecular engineering, data science, and therapeutic applications.
How to Apply for Fully Funded PhD AI Antibody Protein Design At Imperial College London
There is no fixed application deadline listed for this studentship. The process begins by contacting the supervisor directly.
Professor Pietro Sormanni Email: pietro.sormanni@imperial.ac.uk
When reaching out, include:
- A concise cover letter or email explaining your interest in the project and relevant background
- Your CV
- Any examples of coding projects, quantitative work, or research experience (GitHub links are welcome)
Early contact is strongly encouraged. Competitive positions like this move quickly, and starting “as soon as possible” means the supervisor is looking for motivated candidates ready to begin in the near term.
Tips for a Strong Application
Highlight any experience with Python programming, data analysis, machine learning projects, or quantitative coursework. Even if your background is not purely in ML, demonstrate curiosity and self-directed learning through personal projects, online courses, or previous research.
Emphasise any exposure to biology, chemistry, or protein-related topics, but do not worry if your experience is limited — the project explicitly welcomes motivated candidates who want to apply quantitative skills to this domain.
Prepare to discuss why you are excited about therapeutic antibody and protein design specifically, and how you see AI transforming this field over the next decade.
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Take the Next Step Toward Impactful Research
This fully funded PhD studentship offers far more than financial support. It is an invitation to help shape the future of AI-enabled therapeutic design at one of the world’s premier research institutions, under the guidance of a leading expert, in a lab that values both methodological innovation and real-world applicability.
If you are passionate about coding, quantitative problem-solving, and the potential of AI to accelerate the creation of better medicines, this opportunity deserves your serious consideration.
Official scholarship page: https://www.imperial.ac.uk/study/fees-and-funding/scholarships-search/ai-for-antibody-and-protein-design-20262027.php
Supervisor profile: https://profiles.imperial.ac.uk/pietro.sormanni
Reach out to Professor Sormanni today to express your interest and begin the conversation. The future of computational antibody and protein design is being written now — and there is a place in that story for motivated researchers ready to contribute.
Applications are open and the start date is flexible. The sooner you connect with the supervisor, the sooner you can begin exploring this exciting frontier.