NMR Manager

Imperial College London
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

The Centre undertakes metabolic phenotyping from a diverse portfolio of research collaborations spanning clinical medicine to large-scale population health studies from within Imperial and beyond. The NPC specialises in state-of-the-art targeted and untargeted approaches using a combination of NMR, chromatographic and MS technologies alongside innovative informatics approaches. With a key focus on building strong research collaborations, identifying biologically and clinically relevant biomarkers, and developing innovative technological and bioinformatics approaches required for the analysis of “big data”, this is an exciting role within a highly motivated and supportive team.

The Centre, in operation since June 2012 (previously as the National Phenome and Clinical Phenotyping Centres), provides high-throughput, high-quality analysis of samples and engages in academic and industrial collaborations for projects requiring complex data analysis and interpretation across a wide range of research areas. It is a national resource to enable researchers (academic and commercial) to have access to state-of-the-art NMR and MS facilities and expertise at Imperial to undertake this work.

The post-holder will form part of a team ensuring the smooth and efficient running of the NMR facility of the National Phenome Centre (NPC) at the Hammersmith campus including data collection, review of data quality, and requisite data processing.


The post-holder will manage the day to day delivery of the National Phenome Centre NMR lab and analytical service ensuring that projects are delivered on time and to budget. The postholder will achieve this through leadership and management of a team of technicians and post-doctoral staff (currently 2 staff). The post-holder will ensure that the laboratory and equipment are well maintained and efficiently run and the data produced are of very high quality. The post-holder will have an important role in ensuring good communications with colleagues in the National Phenome Centre, and with other related parts of the Department such as Division of Systems Medicine and with our customers and partners in other organisations.


Higher degree in Chemistry, Physics or equivalentProven ability in all aspects of practical NMR spectroscopy including experience with biological sample handling including instrument maintenance and troubleshooting, method development and validation and metabolite identificationDemonstrate management of a multi-user instrument laboratory running an analytical service and will be happy working with a range of customers. Good leadership and motivational skills will be essential as will excellent communication skillsAbility to work to deadlines whilst maintaining a high level of accuracy.
The opportunity to develop new research and collaboration opportunities in NMR.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.