Research Assistant or Research Fellow in Bioinformatics

Cranfield University
Cranfield
1 year ago
Applications closed

Related Jobs

View all jobs

Data science programme lead

Data science programme lead

Research Data Analyst

Data science programme lead

Senior Machine Learning Engineer - Research

Executive Director: BHF Data Science Centre

Role Description

We are seeking a passionate and skilled researcher to join our groundbreaking research on climate-informed food safety and pathogen impact modeling at Cranfield University.

About the Role

You will join the Bioinformatics genomic informatics team as part of the Horizon-Europe’s . The project aims to enable for the first time a holistic, systemic approach to food safety risk assessment across the supply chain in view of climate change models with the use of digital technologies, including AI.

This role involves leading innovative research to develop climate-informed impact models for enteric pathogens in fresh produce and mycotoxin contamination in short-grain cereals under varying climate scenarios. You will conduct climate-controlled experiments to study mycotoxin production, generate transcriptomic profiles using RNA-Seq, and identify key mycotoxin-producing gene clusters. Leveraging integrated transcriptomic and climate data, you will develop and optimize predictive models for forecasting mycotoxin levels, ensuring precision beyond traditional approaches. The role also involves collaborating with international partners, contributing to teaching activities, and disseminating research findings through high-impact publications and conferences.

About You

You will have a PhD in Bioinformatics, Systems Biology, Microbiology, Molecular Biology, or a related field (or be close to completion). For Research Assistant, you will have an MSc in Bioinformatics or related fields with demonstratable experience in genomic and transcriptomic analysis. You should demonstrate expertise in designing and conducting growth chamber experiments, as well as bioinformatics skills for RNA-Seq transcriptomic analysis, including assembly, differential expression, and functional annotation. Experience with molecular biology techniques and a strong understanding of Fusarium species and mycotoxin contamination mechanisms are ideal. Proficiency in using high-throughput sequencing technologies, computational tools, and scripting languages (e.g., Python, R) is required. Familiarity with predictive modeling techniques and integrating climate data with biological data will be advantageous.

About Us

As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact .

The Bioinformatics Group at Cranfieldis a computer-based group with research focusing on the development and application of computational methods and AI in order to unravel the complexity of biological systems. We run and administer our in-house high-performance-computing facility for NGS data analysis, including de-novo assembly, global transcriptomics, and genotyping. We also have a long track record in data science and machine learning.

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more .

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity . Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working .

Working Arrangements

Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners.

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.