Call for Associate Editor Applications: Computation, AI and Machine Learning

IOM3
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist (Government)

Graduate Machine Learning Researcher

Staff Machine Learning Engineer

Principal Data Scientist - 6929

MLOps Engineer

Salary

Monetary Stipend Overview is a JCR-ranked, peer-reviewed academic journal owned by the Institute of Materials, Minerals and Mining (IOM3) and published by Sage Publishing. The journal is published eighteen times a year. IOM3 and Sage are seeking two dynamic and innovative Associate Editors for Materials Science and Technology (MST): one specialising in Functional Materials, and the other in Computation, AI, and Machine Learning in Materials Science. This is an exciting opportunity to shape the future of the journal during a period of transition, working closely with the new Editor-in-Chief, Radhakanta Rana, to define long-term strategy under a refreshed editorial structure. As part of this forward-thinking team, the successful candidates will play a pivotal role in setting the research agenda, engaging with leading global researchers, and ensuring the journal remains at the forefront of its field. Successful applicants will be leaders at the cutting edge of their field, who are passionate about advancing knowledge and influencing the next era of materials science. The aims & scope of the journal will shortly be updated, which will reflect the journal’s new editorial structure.
Details For this position (Associate Editor for Computation, AI and Machine Learning in Materials Science), the successful applicant will be able to demonstrate broad expertise in Computation, AI and Machine Learning, including though not limited to: Machine learning for materials design Microstructure classification Data-driven materials discovery Process optimisation Computational metallurgy Materials Science and Technology is an international forum for the publication of refereed contributions covering fundamental and technological aspects of materials science and engineering. The journal has a particular interest in the continuum from understanding of process routes leading to the generation of microstructure, through characterisation and understanding of how microstructure is controlled and manipulated, to the control and prediction of relevant engineering properties. 'Microstructure' is shorthand for nano/micro/meso/macrostructure, provided that 'structure' is identified at the appropriate size scale. 'Properties' may be electrical, mechanical, electronic, chemical, magnetic, thermal, optical, or biochemically related. Reports of the use of modelling, informatics and related approaches to enhance understanding and predict properties must include validation against experimental results. Contributions addressing any part of the continuum in an insightful manner, whatever the material system, are invited. What is important is that an attempt is made to relate 'properties' back to effects of 'microstructure'. Materials Science and Technology is indexed in the Clarivate Science Citation Index Expanded (SCIE). Editorial responsibilities Contributing to the overall vision and direction of the journal in collaboration with the Editor-in-Chief, Editors, members of the editorial board, IOM3, and Sage. Working with the Editor-in-Chief, Radhakanta Rana, and Editors to determine the content of each issue, including commissioning of key papers and special (guest edited) issues. Managing the flow of manuscripts, including reading submitted papers, managing the peer review process, making decisions about rejection, revision and acceptance, and corresponding with authors. Ensuring that publication processes, legal requirements (e.g. copyright), and deadlines are met. Working with the guest editors of special issues. Communicating with the members of the Editorial Board. Promoting the journal internationally, through high quality special issues and key papers, and through attending conferences. Keeping abreast of developments in the field and in academic publishing. Essential Requirements A high academic degree in materials science, with a particular focus on the fields advertised (see list above). At least 5 years of experience in their field (as a professional or as an academic). International reputation in the required fields (see list above). Experience in research project management. International professional and/or academic links to scholars and practitioners in the field of computation, AI and machine learning in materials science. Experience of writing to a publishable standard in peer-reviewed outlets. A commitment to diverse scholarship. Ability to work under pressure and manage responsibilities. Good organisational abilities. Strong interpersonal skills. Ability to work independently. Consideration will also be given to: Experience in editing and publishing. Experience of using ScholarOne Manuscripts, either as author or editor. All enquiries and expressions of interest should be directed via email in the first instance to both: Tia Byer, Journal Relationship Manager, IOM3:  Kate Hall, Senior Publishing Editor, Sage:

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.