National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials

Nottingham Trent University
Nottingham
3 weeks ago
Create job alert

Postdoctoral Researcher in Machine Learning of Porous Molecular Framework Materials

Grade H

£38, - £43, p.a. pro rata

1 year fixed term contract

About the Role

Molecular Framework Materials (MFMs) are of strong interest worldwide due to their porosity, crystallinity and chemical functionalizability / tunability making them appealing for a broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis.
A major drawback when applying computational chemistry to MFMs is that, even for "rigid" MFMs, especially when functionalized, their structures are disordered due to the random orientation of linkers. Such structural disorder and flexibility are well-known in crystallography, but less in computational chemistry, where a distinct structure is required to undertake a calculation. This problem has largely been ignored, and researchers have chosen a single “representative” MFM
structure, ignoring positional disorder which makes the calculation feasible but at the cost of ignoring the local structure. This is problematic, as the local structure has strong effects on guest binding, diffusion and even the dynamic behaviour of the entire MFM.

In this project, funded by the Leverhulme Trust, we will develop a machine learning platform to describe the the effects of isomerization on adsorption and diffusion in porous Molecular Framework Materials (e.g. MOFs, COFs, ZIFs, MOPs...).

The project extends from our initial work on developing desciptors for isomerization in MOFs:

What we are looking for:

A PhD in Chemistry, Materials Science, Physics or a related field

Experience in python (ASE, scikit-learn, pytorch) is essential

Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous

For more details, please take a look at the role profile. We'll still consider applications even if you don't meet every single one of the requirements, so don't be put off if you don't match them perfectly.

Interviews; w/c 14th July

About Us

The School of Science and Technology at Nottingham Trent University (NTU) is an exciting multidisciplinary environment for learning, teaching and research, with some of the best facilities in the UK.

We pride ourselves on delivering high-quality teaching and diverse, real-world research. We specialise in biosciences, chemistry, computing and technology, as well as engineering, forensic science, mathematics, physics and sport science. This mix of traditional and modern subjects encourages and inspires future innovators.

In the Department of Chemistry, courses are taught in modern, innovative spaces, offering excellent career prospects and accreditation by The Royal Society of Chemistry. The Department has an active research community with a diverse range of knowledge and expertise.

For any informal queries about the role, please contactDr Matt Addicoatat.

Join Us

30 - 35 days annual leave per year plus statutory bank holidays and 5 university closure days pro rataHybrid working- we encourage and offer a mixture of office working and working from home. You're empowered to define how you work best for the benefits of your stakeholders.Flexibility- take ownership over how you get your work done. We're open to different working patterns and approaches.Salary Sacrifice Retirement Savings Planwith life assurance and income protection. Available to colleagues who choose to opt out of the contractual pension scheme. Minimum colleague contributions of 0% matched with minimum NTU contributions of 8%. Access to a wealth of formal and informal professional development opportunities to develop your skills and advance your career. Range of health and wellbeing services, including a Health Cash Plan, voluntary benefits, discounts, and savings for all colleagues. And a whole lot more…Find out more about the range of benefits we offer at

Come and be part of our success. Apply today.

Safe and Inclusive

At NTU, we continue to build an inclusive culture that encourages, supports and celebrates the diverse voices and experiences of our students and colleagues. By championing positive wellbeing, we promote an environment where all can thrive and reach their full potential. We welcome the unique contributions that you can bring and we encourage people from underrepresented communities and backgrounds to apply to join our team.

Please note that unfortunately, this role has been assessed as ineligible for sponsorship under the UK Visas & Immigration points-based immigration system however, we recommend that you assess your eligibility before applying for this position. For more information visit the Government Skilled Worker visa support page. However, applications are welcome from candidates who do not currently have the right to work in the UK, but who would be eligible to obtain a valid visa via another route. Please consult the for further information.

Please note that this role is covered by the Rehabilitation of Offenders Act and successful applicants will be asked to declare any unspent criminal convictions.

Related Jobs

View all jobs

Senior Data Scientist

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.