Research Assistant - Deep learning for Genomics (Bornelöv Group)
Company
Location
Cambridge, England, United Kingdom
Posted At
6/25/2025
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Description
We are seeking a highly motivated and talented research assistant to join us at the Department of Biochemistry, University of Cambridge, to study gene regulation using deep learning. This is an exciting opportunity to use AI-based methods to uncover the molecular mechanisms behind mRNA processing and fate.
You will be part of a computational team, led by Dr Susanne Bornelöv, which studies the role of codon usage bias in gene regulation using various approaches including machine learning and AI, evolutionary genomics, and sequencing data analysis.
Your project will focus on using deep learning and other statistical and machine learning approaches to reveal how codon usage bias and other mRNA features contribute to gene regulation. The ultimate aim is to gain a precise understanding of how these different properties interact to influence mRNA localisation, stability and translation, as well as protein function. To achieve this, you will use cutting-edge computational approaches, including building in silico models that enable you to systematically probe the effect of differences in codon usage and nucleotide sequence on mRNA fate.
To be successful in this role, you will need experience in deep learning or other machine learning techniques, an ability to drive a project independently, and solid programming/scripting skills. Applicants should have a BSc or MSc degree in a relevant quantitative discipline and ideally some research experience. Prior work involving any aspect of gene regulation, including mRNA transcription, translation or turnover would be beneficial, but is not strictly required. Most importantly we are looking for someone with a strong desire to be part of a team aimed at uncovering fundamental aspects of gene regulation using computational methods.
For more information about the research group, including our most recent publications, please see our website: www.sblab.uk.
Fixed-term: The funds for this post are available for 1 year, starting from the successful candidates start date. The starting date is flexible, but suggested to be around September.
Please send applications in the following format: a CV, including full details of all University courses taken with date (with grades if available), a cover letter, and the names and contact details of two academic referees. Please use the cover letter to explain why you are applying for this role, what you will bring to the project, and how you match the essential and desired criteria for the post (please see the Further Particulars document).
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
For any informal enquiries, please contact Dr Susanne Bornelöv via: [email protected]
For queries regarding the application process, please contact: [email protected]
Please quote reference PH46415 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
Further information
You will be part of a computational team, led by Dr Susanne Bornelöv, which studies the role of codon usage bias in gene regulation using various approaches including machine learning and AI, evolutionary genomics, and sequencing data analysis.
Your project will focus on using deep learning and other statistical and machine learning approaches to reveal how codon usage bias and other mRNA features contribute to gene regulation. The ultimate aim is to gain a precise understanding of how these different properties interact to influence mRNA localisation, stability and translation, as well as protein function. To achieve this, you will use cutting-edge computational approaches, including building in silico models that enable you to systematically probe the effect of differences in codon usage and nucleotide sequence on mRNA fate.
To be successful in this role, you will need experience in deep learning or other machine learning techniques, an ability to drive a project independently, and solid programming/scripting skills. Applicants should have a BSc or MSc degree in a relevant quantitative discipline and ideally some research experience. Prior work involving any aspect of gene regulation, including mRNA transcription, translation or turnover would be beneficial, but is not strictly required. Most importantly we are looking for someone with a strong desire to be part of a team aimed at uncovering fundamental aspects of gene regulation using computational methods.
For more information about the research group, including our most recent publications, please see our website: www.sblab.uk.
Fixed-term: The funds for this post are available for 1 year, starting from the successful candidates start date. The starting date is flexible, but suggested to be around September.
Please send applications in the following format: a CV, including full details of all University courses taken with date (with grades if available), a cover letter, and the names and contact details of two academic referees. Please use the cover letter to explain why you are applying for this role, what you will bring to the project, and how you match the essential and desired criteria for the post (please see the Further Particulars document).
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
For any informal enquiries, please contact Dr Susanne Bornelöv via: [email protected]
For queries regarding the application process, please contact: [email protected]
Please quote reference PH46415 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
Further information
- Further Particulars - Research Assistant - Bornelöv Group
Advertise with us by contacting: [email protected]
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