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The role
We are seeking a Postdoctoral Research Fellow for a (up-to) 3-year position to work on an exciting project funded by the Medical Research Council and led by Dr Bingxin Lu.
This project aims to reconstruct the evolutionary dynamics of chromosomal instability in cancer genomes. Understanding how chromosomal instability evolves during the progression from precancerous lesions to malignancy, metastasis, and treatment resistance is important for cancer detection and treatment. Building upon our previously published method, CNETML (doi.org/10.1186/s13059-023-02983-0), this project will develop more realistic evolutionary models of copy number alterations and better phylogenetic inference methods with wider applicability. The developed methods will then be applied to extensive publicly available datasets to investigate the dynamics of chromosomal instability. Our new methods will enhance the toolkit for cancer researchers and drive broader applications of cancer phylogenetic inference. The insights gained from the inferences will also support personalized therapy and advance public health efforts.
The successful candidate will not only design and conduct the research but also write and communicate results as scientific papers and in scientific presentations at national and international conferences.
The successful candidate will have a strong research background and should have a PhD (or close to completion) in computational biology, bioinformatics, computer science, or related fields that include a substantial amount of computing and mathematical/statistical modelling, ideally related to phylogenetic method development. Working knowledge in genomics, evolutionary biology, cancer biology, or population genetics is desirable. Experience in programming is essential.
Qualifications, experience and knowledge
PhD (or close to completion) in computational biology, bioinformatics, computer science, or related fields that include a substantial amount of computing and mathematical/statistical modeling, ideally related to phylogenetic method development E (Essential)
A strong record of publications in international, peer-reviewed journals D (Desirable)
Working knowledge in genomics, evolutionary biology, cancer biology, or population genetics D
An undergraduate degree in computer science, mathematics, statistics, physics, engineering, or another quantitative discipline D
Skills and abilities
Proficiency with Unix-based systems and programming, ideally in multiple languages, including but not limited to C++/C, Python, and R E
Solid mathematical and statistical modeling skills, ideally in Markov models and Bayesian statistics E
Excellent English and communication skills E
Experience in designing and implementing algorithms, ideally related to optimization and Markov chain Monte Carlo (MCMC) techniques E
Experience in developing and deploying software in academic or industry settings D
Experience in high-throughput sequencing data analysis, ideally using compute clusters or cloud computing D
Personal attributes
Strong commitment to rigorous and high-quality reproducible research E
Highly motivated, curious, and enthusiastic in research E
Openness to learn new techniques in a fast-moving interdisciplinary field E
Ability to work both independently and collaboratively while maintaining integrity and accountability E
Ability to work harmoniously with colleagues and students from diverse cultures and backgrounds E
Please submit your cover letter, CV, and contact of three references with your application on the University of Surrey Website.
For informal inquiries, please contact Dr Bingxin Lu b.lu@surrey.ac.uk.