About the Project
Project overview
Gene regulation is a fundamental aspect of life, in which cells control when and to what extent specific genes are activated to produce a protein. This process is tightly controlled using both transcriptional and posttranscriptional mechanisms. The Bornelöv lab investigates these molecular mechanisms using cutting-edge computational and AI-based methods. We are particularly interested in how higher layers of information, such as codon usage, contribute to posttranscriptional gene regulation and…
how this has shaped genome organisation over evolutionary time.
To address these questions, we combine (multi-)omics data analysis, machine learning and artificial intelligence, and comparative genomics to elucidate the underlying molecular mechanisms, including codon optimality-mediated mRNA decay. For example, by analysing ribosome profiling data, we are able to explore ribosome occupancy at single-codon resolution, while AI-based models allow us to build in-silico systems to study fundamental principles underlying gene expression and protein synthesis. In parallel, we also use fruit flies (Drosophila) as a model organism to identify mechanisms that drive the evolution of codon usage bias and genome organisation.
We are currently seeking a PhD applicant interested in starting October 2027. Potential research areas include (but are not limited to):
- Codon-level prediction of translational efficiency
- Use of explainable AI to uncover rules of gene regulation
- Comparative analysis of codon usage bias across the tree of life
- Characterisation of tRNA supply and demand
- Development of RNA-focused foundation models
- Using deep learning to design new regulatory elements
- Understanding the impact of transposable elements on gene regulation
If you are passionate about exploring fundamental molecular biology principles using computational biology, machine learning, and/or evolutionary genomics, please see our website for further information: https://www.sblab.uk
The role will offer extensive training in bioinformatics and machine learning to study molecular biology. There may also be scope to contribute to experimental validation of key discoveries if desired.
Preferred skills/knowledge
We are looking for someone with a keen interest in computational molecular biology, with good computer and coding skills, and a willingness to uncover fundamental workings of the cell. The ideal candidate will have a degree in computational or systems biology, bioinformatics, computer science or related disciplines. Applicants with a background in e.g., molecular biology or biochemistry and with clear evidence of computational skills and interest are also highly encouraged to apply.
Application deadline
Please express your interest at least a month before the funding deadlines (see relevant dates below).
How to apply
Please apply by sending an email to smb208@cam.ac.uk with the subject “SBlab-PhD2027”. Your application should include:
- Academic transcripts
- CV (max two pages)
- A statement of interest (max 500 words)
- A brief outline of a research proposal (max 300 words)
- Contact details of two academic references
Please ensure you explain why you wish to pursue a PhD in this area, outline your research interests, and describe the skills and research experiences you will bring to the role within your statement of interest.
The purpose of your research proposal is for me to understand what type of research questions or approaches you might be interested in. It can be a draft at this stage and may change significantly.
Interviews and outcome
Interviews will be held in November (or earlier) and the successful candidate(s) will be supported to submit a formal application through the University’s Application Portal before the University’s funding deadline (8 December 2026).
Funding Notes
This is not a funded PhD programme, and candidates are required to secure their own funding.
After contacting me, selected applicant(s) will be invited to submit a formal application through the University’s Application Portal before the University’s funding deadline (8 December 2026). Please note that an earlier funding deadline (14 October 2026) applies to US applicants to be eligible for Gates Cambridge.
If your formal application is successful, you will be considered for the local funding competition (e.g., Cambridge Trust or Gates Cambridge) and be supported to apply for additional funding schemes, including overseas if applicable.
References
- Dash T, Bornelöv S. Predicting gene expression using millions of yeast promoters reveals cis-regulatory logic. Bioinformatics Advances vbaf130. https://doi.org/10.1093/bioadv/vbaf130
- Rafi AM, Nogina D, Penzar D, Lee D, Lee D, Kim N, Kim S, Shin Y, Kwek I-Y, Meshcheryakov G, Lando A, Zinkevich A, Kim B-C, Lee J, Kang T, Vaishnav ED, Yadollahpour P, Random Promoter DREAM Challenge Consortium (72 members, including Bornelöv S), Kim S, Albrecht J, Regev A, Gong W, Kulakovskiy IV, Meyer P, de Boer CG. A community effort to optimize sequence-based deep learning models of gene regulation. Nature Biotechnology s41587-024-02414-w. https://doi.org/10.1038/s41587-024-02414-w
- Bornelöv S. A code within the genetic code. Nat Rev Mol Cell Biol. 2024 25(6):423. https://doi.org/10.1038/s41580-024-00724-0
- Bornelöv S, Selmi T, Flad S, Dietmann S, Frye M. Codon usage optimization in pluripotent embryonic stem cells. Genome Biol. 2019 Jun 7;20(1):119. https://doi.org/10.1186/s13059-019-1726-z
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