We are seeking a highly motivated and talented postdoctoral research associate to join our team at the CRUK Cambridge Institute (part of the University of Cambridge) to study genome organisation and regulation using deep learning approaches. This is an exciting opportunity to contribute to our goal of understanding the molecular mechanisms of gene regulation.
You will be part of a computational team, led by Dr Susanne Bornelöv, which studies gene regulation using various approaches including machine learning, evolutionary genomics, and sequencing bioinformatics. Our team is funded by an 8-year Wellcome Career Development Award (wellcome.org/grant-funding/people-and-projects/grants-awarded/data-driven-methods-unravel-hidden-layers-genome).
Your project will focus on using deep learning and other statistical and machine learning approaches to understand the role of codon usage in gene regulation. The ultimate aim is to understand how different parts of an mRNA contribute to mRNA localisation, stability and translation, as well as to protein function. This will be achieved through innovative computational approaches, including building in silico models that will enable us to systematically probe the effect of differences in codon usage and nucleotide sequence.
To be successful in this role, you will need to have substantial 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 PhD (or be about to receive one) in a relevant quantitative discipline. You will be working with large-scale genomics data from many species (mammals or insects), and prior work involving any aspect of gene regulation, including mRNA transcription, translation or turnover, would be highly beneficial. 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 approaches.
As a member of our team, you will have the opportunity to contribute your own ideas to ongoing research activities. You will also have the freedom to undertake independent research in the context of the general research area of the group.
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 3 years in the first instance.
The closing date for applications is 29th May 2023. Interviews will be held in the beginning of June. The starting date is flexible, but ideally in September.
Essential:
* Ph.D. in Computational Biology, Bioinformatics, Computer Science, Mathematics, Statistics, or a related discipline
* Ability to plan, execute and record scientific studies
* Excellent programming and scripting skills
* Experience applying and developing machine learning techniques
* Excellent communication and interpersonal skills and attention to detail, ability to work effectively as part of a team
* Ability to manage own workloads and meet deadlines
* Ability to present scientific data and to interact with scientific colleagues
* Track record of contributions to a scientific area, demonstrated by peer-reviewed publications, preprints and/or through conference presentations
Desirable:
* Experience building deep learning models using frameworks such as TensorFlow or PyTorch
* Prior experience working with any aspect of genome organisation or gene regulation
* Experience working with high-throughput sequencing and genomics data
* Experience using high-performance computing and version control systems
Please apply following the instructions on the website: www.jobs.cam.ac.uk/job/40623/
The application deadline is 29th May