Ref Number
B02-10811
Professional Expertise
Research and Research Support
Department
School of Life & Medical Sciences (B02)
Location
London
Working Pattern
Full time
Salary
See advert text
Contract Type
Fixed-term
Working Type
Hybrid
Available for Secondment
No
Closing Date
12-Jul-2026
We are looking for a PhD student with strong computational skills interested in structural bioinformatics, and infection biology. The PhD project will be supervised by Dr. Gorka Lasso (https://profiles.ucl.ac.uk/103340-gorka-lasso-cabrera) at the Institute of Infection, Immunity and Transplantation (https://www.ucl.ac.uk/medical-sciences/divisions/infection-and-immunity). Dr. Gorka Lasso, the primary supervisor, holds a joint appointment in the Institute of Infection, Immunity and Transplantation and the Institute of Structural and Molecular Biology (https://www.ismb.lon.ac.uk). He has extensive research experience in the field, and he is passionate about training the next generation of computational biologists.
The Lasso lab (https://lassolab.co.uk) works at the intersection of computational biology and virology. The lab applies computational approaches—including structural bioinformatics, network biology, and machine learning—to study sequence-to-structure-to-function relationships in viral and host proteins that drive infection by (re)emerging zoonotic viruses. The laboratory thrives on collaboration, working closely with both experimental and computational groups within the UK and globally to drive multidisciplinary discoveries. We are committed to fostering an inclusive, supportive, and intellectually stimulating environment.
How do viruses rewire host cells during infection, and can AI help us predict these interactions at scale? This PhD project will develop next-generation computational approaches to identify pathogen-host protein-protein interactions (PPIs) using advances in machine learning and structural biology.
Recent advancements in machine learning and AI have revolutionized structural bioinformatics, significantly improving protein and protein complex structure prediction. However, predicting pathogen-host protein-protein interactions (PPIs) remains a challenge due, partly, to limited data and conflicting evolutionary pressures. This, however, contrasts with the importance of such interactions as they mediate essential steps during infection. This is particularly relevant in viral infectious diseases, where viral proteins interact with host proteins to co-opt cellular processes that are essential for the viral replication cycle. In this regard, knowledge of pathogen-host PPIs is critical for understanding the biology underpinning infection and for designing novel therapeutic approaches.
This project will develop a new computational method to predict pathogen-host protein-protein interactions (PPIs) by integrating recent developments on AI and structural bioinformatics. The student with implement computational tools, analyse large-scale biological datasets, and collaborate with other computational and experimental researchers. While focusing on pathogen-host PPIs, the framework developed will have broad applications across biology.
The project offers an opportunity to gain research training in AI, structural bioinformatics and computational biology, one of the fastest growing areas in biology today.
We are looking for a motivated and committed individual who is excited to contribute to advances in structural bioinformatics and computational biology. We will consider students from a STEM discipline (e.g. computer science, physics, biochemistry, chemistry…) and provide training as necessary to work in the interdisciplinary environment required. However, willingness to engage in advanced computational methods is essential. Experience with programming and Linux/Unix environment would be advantageous.
Desirable skills include:
- Programming experience (Python, or similar)
- Experience working in Linux/Unix environments
- Familiarity with biological data analysis
- Interest in machine learning and structural biology
- Strong quantitative and problem-solving skills
Eligibility
Applicants must meet the eligibility requirements for Home fee status. You must have (or about to be awarded) a First or Upper Second (2.1) Bachelor and/or Masters level degree in a relevant subject.
Application process
Please prepare a single PDF document in the following order:
- A covering letter outlining motivation, interest, and suitability for this project
- CV / resume
- Contact details for two academic referees
If you have any queries about the role or application process, contact [email protected]. If you have any technical issues, or need reasonable adjustments or a more accessible format to apply for this job online, please contact the staffing team at [email protected].
This is a fully funded 3-year PhD studentship funded by the Institute of Infection, Immunity and Transplantation at UCL. The studentship covers tuition fees at home rate, and a non-taxable annual stipend of £24,643 per year.
The student will join a growing and collaborative research group and will have opportunities to interact with research across UCL, and other national and international collaborators. The successful candidate will receive training in structural bioinformatics, machine learning, scientific programming, high-performance computing, and reproducible research practices. We offer a collaborative, inclusive, and multidisciplinary environment with access to advanced computational resources, including GPU-enabled HPC clusters and high-end workstations.
This setting emphasizes innovation, teamwork and mentorship, providing an ideal platform to carry out the proposed project while developing transferable skills in programming, data science, protein modelling, and machine learning. This PhD project will equip you with a versatile skill set suited for careers in academia, biotechnology, or pharmaceutical research.
As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce. These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.
Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality.
