Computational Biologist in Single Cell Genomics

University of Oxford
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer: Build Scalable Biotech Pipelines (Hybrid)

Senior Scientific Data Engineer, Data Platform

Data Engineer

Bioprocess Upstream Data Scientist

Technical Program Manager - Machine Learning - New York

Technical Program Manager - Machine Learning - New York

Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, MRC WIMM Centre for Computational Biology, John Radcliffe Hospital, Headington, Oxford At the (CCB), we work alongside scientists and clinicians to realise the potential of ‘big data’ in biology by exploiting complex information to make discoveries that benefit human health. The CCB encompasses an international team of over 40 computational biologists, working closely with 500 lab-based scientists and clinicians. As part of a Wellcome Trust Collaborative award, applications are invited for a highly motivated individual to lead the analysis of single-cell gene expression and open chromatin profiling data, investigating transcriptional regulation during Drosophila Melanogaster brain development. You will work closely with experimental collaborators within the to answer important questions in developmental neuroscience. You will lead the analysis of single-cell RNAseq & ATACseq profiles from hundreds of thousands of neurons, representing the entire fruit fly midbrain, from flies across development stages, to build a map of neuronal development and understand how sex-specific neuronal identity emerges from transcriptional programmes. With a PhD or MSc in a quantitative discipline (e.g. bioinformatics, computational biology, physics, statistics, engineering or mathematics), you will have experience of working in a Linux environment and be proficient in Python and R. Excellent interpersonal and communication skills, with the ability to convey concepts to other scientists in different fields of research are essential. Experience in analysis of single cell gene expression data is highly desirable. The position is available fixed-term until 31st March 2026, funded by the Wellcome Trust.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.