|The starting salary will be from £41,732 per annum pro rata on Grade F, depending on qualifications and experience.
|8th December 2023
|31st January 2024
The above full-time (1.0 FTE) post is immediately available until 1st October 2026 on a fixed-term basis.
This role offers the opportunity for hybrid working – some time on campus and some from home.
We will be reviewing applications to this role on a rolling basis as they come in, and may close the advert at any time when a suitable candidate is found.
The Faculty wishes to recruit a computational Postdoctoral Research Fellow to participate in a Wellcome funded interdisciplinary project studying the sequence code underlying regulatory regions in disease. This post is available immediately for three to four years.
The successful applicant will lead analysis efforts to understand how changes in regulatory sequences impact gene regulation through transcription factor binding. The applicant will study how multiple transcription factors combine to regulate gene expression with single-molecule footprinting data (also known as NOMe-seq, Fiber-seq, SMAC-seq, nanoNOMe) with Oxford Nanopore Technologies long-read sequencing.
The post will focus on understanding defects in gene regulation causing rare and common forms of glucose regulation disorder, from neonatal diabetes to type 2 diabetes. The applicant will analyse and develop methodologies for data generated by the group during pancreas development and function.
The post will be supervised by Dr. Nick Owens at the University of Exeter. This post can be tailored to the applicant’s expertise and can encompass analysis of functional genomic data, Bayesian models of single-molecule footprinting data employing chromatin physics, deep convolutional neural networks predicting function from DNA sequence, and disease variant annotation.
This post is an opportunity to use and develop state of the art data science and artificial intelligence methods to understand new genomic data available from single-molecule footprinting. The post will be a pivotal member of a team working to gain insights into how regulatory regions control gene expression during developing and adult cells and tissue, and to understand how changes to regulatory sequences result in disease. The applicant will join a world-leading centre for diabetes research at the University of Exeter.
The position will come optional provision for cross-disciplinary training providing the computational applicant experience in the lab generating data.
You can learn more about our research at https://owensnick.github.io/.
We encourage applicants from a broad range of backgrounds, including those from a numerate background but no prior expertise in biology who are enthusiastic to move into this area.
Applicants will possess a relevant PhD and will have experience relevant to the role. Applicants should demonstrate that they have experience in at least one of the following areas: analysis of gene regulation through genomic data (transcriptomics, transcription factor binding, chromatin accessibility; Oxford Nanopore Technologies long read methylation data); expertise in data science/machine learning; Bayesian hierarchical models; probabilistic programming languages; and convolutional neural networks. Experience in a range of programming languages, particularly the Julia programming language, will be desirable.
For further information please contact Dr. Nick Owens, e-mail firstname.lastname@example.org
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