Research Associate - Data-Driven & Quantum Materials Science in Manchester

The current opportunity is available for current Manchester Met students, to apply you must be based in the UK for the duration of the role. The main purpose of this role is to provide research support to colleagues and support staff for the Towards Automated Materials Synthesis: Exploring Classical and Quantum Machine Learning Approaches Project by assisting, and where necessary taking the lead, with research data collection and analysis, and the dissemination of findings to appropriate groups related to the project.

Conduct a systematic literature review and meta-analysis of high entropy alloys (HEAs) and metal-organic frameworks (MOFs) relevant to hydrogen storage and fuel cell applications, with particular focus on identifying transferable descriptors and performance metrics. Undertake computational and data-driven research using classical machine learning, quantum machine learning, and hybrid modelling approaches. Employ specialist computational tools and programming environments (e.g. Python-based ML frameworks, materials informatics libraries, and quantum ML toolkits) requiring a high level of technical expertise, precision, and reproducibility in data handling and model development. This role is computational in nature and does not require routine work in hazardous environments or the use of protective clothing beyond standard office and computing laboratory requirements.

Plan, prioritise and organise own work or resources, and the work of Research Assistants and students where appropriate, in order to achieve agreed objectives. Conduct literature and database searches using standard techniques and methods. Write up results of own research and prepare for presentation to research team and relevant stakeholders. Contribute to the production of research reports and publications. Use initiative and judgement to develop appropriate techniques in order to facilitate research work and resolve problems affecting the achievement of objectives and deadlines.

Liaison and Networking

Present research to the team and in appropriate articles for publication, such as academic/professional journals or conferences. Participate in internal and external partnerships and networks in order to aid the dissemination of research findings, to share best practice, and to enhance the reputation of the University.

Service Provision

Develop own and/or joint research objectives, budgets, and project proposals with senior research staff which meet funding criteria and benefit the department. Proactively and effectively engage with quality assurance procedures to ensure that University standards are upheld. Collaborate with academic colleagues on research development and future direction.

Team Working

Actively participate as a member of the research team, offering general help and guidance to others and PhD students who may be assisting with the research project. Attend Faculty, Department and Programme meetings/boards as appropriate to proactively contribute to decision making. Introduce new starters to the area, giving training on basic skills and activities to assist their induction to the team. Assist senior staff in the guidance and support of new research assistants or members of staff within the department as appropriate. Supervise the work/projects of taught postgraduate and/or research students as required to support the development of students' research skills.

Miscellaneous

You have a legal duty, so far as is reasonably practicable, to ensure that you do not endanger yourself or anyone else by your acts or omissions. In addition, you must cooperate with the University on health and safety matters and must not interfere or misuse anything provided for health, safety and welfare purposes. You are responsible for applying the University's Equal Opportunities Policy in your own area of responsibility and in your general conduct. You have the responsibility to promote high levels of customer care within your own areas of work. Such other relevant duties commensurate with the grade of the post as may be assigned by the Manager in agreement with you. Such agreement should not be unreasonably withheld. You have the responsibility to engage with the University's commitment to Environmental Sustainability in order to reduce its waste, energy consumption and carbon footprint. You have the responsibility to engage with the University's commitment to delivering value for money services that optimise the use of resources and therefore should consider this when undertaking all duties and aspects of your role.

Qualifications and Experience

Studying for, or holding a post graduate degree, equivalent qualification or substantial relevant experience, and evidence of continuous professional development in computational materials modelling, machine learning, or quantum technologies. Ability to undertake research by preparing, setting up, conducting and recording the outcome of experiments and/or field work. Ability to use initiative, creativity and judgement to develop appropriate approaches in order to further research and scholarly activities. Proficiency in the use of relevant software packages such as Python, machine learning frameworks (e.g. TensorFlow, PyTorch, scikit-learn), materials databases and informatics tools (e.g. Materials Project, MOF databases), and quantum computing or quantum machine learning toolkits. Possess sufficient breadth or depth of materials informatics, machine learning, and/or computational materials science knowledge to work within an established research team. Experience of externally or internally funded research. Experience of data collection. Experience of presenting and communicating complex information to a range of audiences, both orally and in writing. Experience of prioritising work independently and as part of a team in order to deliver individual and team objectives. Experience of supervising student work. Ability to work effectively across disciplines and engage with researchers from materials science and computer science backgrounds.

Desirable

Knowledge of high entropy alloys (HEAs), metal-organic frameworks (MOFs), hydrogen storage materials, or fuel cell materials, including an understanding of relevant research methods and techniques in the area. Familiarity with quantum machine learning, hybrid quantum-classical algorithms, or high-performance computing environments applied to scientific research. Manchester Metropolitan University is committed to supporting the rights, responsibilities, dignity, health and wellbeing of staff and students through our commitment to equity, diversity and inclusion.