PhD studentship; Lasso Group

The Lassolab () 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 will 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.

• 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

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.

This is a fully funded 3‑year PhD studentship funded by the Institute of Infection, Immunity and Transplantation  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…