Research Associate in Digital-Twin of Fischer-Tropsch Reactors (DigiTwin-FT)

Overview

Working under the guidance of Professor Mo Zandi and Dr James McGregor, the post-holder will be working on the Great British Chemicals funded project DigiTwin-FT. GBC is a National Research & Innovation Centre for Sustainable Industrial Futures funded by EPSRC and NERC, the centre will unite the UK chemical industry, academic researchers and policy makers to advance innovative, scalable and sustainable technology solutions for defossilising chemicals manufacturing. We aim to create people-focussed opportunities to truly shape sustainable carbon management.

The post-holder will be part of the DigiTwin-FT project team working to develop and establish the foundational building block for scaling up CO2-to-fuel Fischer-Tropsch (FT) reactors using a predictive digital twin. Leveraging the University of Sheffield’s existing pilot-scale infrastructure, we will synthesise operational dataset inputs (CO2, H2) to validate a high-fidelity digital twin. Developed in co-creation with our non-academic partner, Siemens, this digital framework will simulate thermo-kinetic behaviours at commercial scales. The project delivers an early-stage techno-economic and life-cycle scoping model, mitigating scale-up risks and accelerating the UK's transition to manufacturing domestic, circular chemicals from waste carbon.

The post-holder should have a background in chemical process engineering, with a strong background in process modelling and digital twins. An understanding of carbon dioxide utilisation is preferred though not essential as immersive training will be provided. This is a unique opportunity to work on cutting edge research with some of the leading players in industry and academia with a focus on reducing greenhouse gas emissions while creating value added chemicals within a circular carbon economy. The successful candidate will perform digital twin development, data analysis and will be able to gain experience working with Siemens commercial software.

Main duties and responsibilities

• Lead the extraction, cleaning, and structuring of historical and live operational datasets from the pilot-scale Fischer-Tropsch (FT) reactor.
• Develop, calibrate, and validate hybrid physics-informed and surrogate models integrating multi-phase fluid dynamics, reaction kinetics, and heat/mass transfer.
• Computationally extrapolate the pilot-scale twin to commercial boundary conditions, virtually mapping thermal runaway hotspots, fluid dynamic bottlenecks, and catalyst deactivation profiles.
• Execute early-stage TEA and screening LCA scoping models using simulated commercial outputs to evaluate levelised product costs and carbon mitigation metrics.
• Drive the day-to-day delivery of Work Packages, taking ownership of deliverables to ensure strict adherence to timelines.
• Maintain clean, robust, well-documented, version-controlled code repositories and reproducible data pipelines.
• Collaborate directly with industrial partners and software providers (e.g., Siemens) to align digital twin architectures with commercial engineering design workflows.
• Coordinate knowledge exchange across the Great British Chemicals (GBC) community, sharing non-proprietary model architectures and methodology frameworks per State Aid guidelines.
• Lead the drafting and submission of high-quality research manuscripts for peer-reviewed chemical engineering, process systems, and computational journals.
• Work with the PI and University commercialisation officers to identify, document, and protect novel foreground IP (e.g., proprietary kinetic algorithms and reactor control loops).
• Assist the team in preparing co-created follow-on research and commercialisation grant proposals leveraging the project's digital baseline.
• Embed Responsible Research and Innovation (RRI) and Equality, Diversity, and Inclusion (ED&I) principles into activities, making ethical decisions aligned with the University sustainability strategy.
• Ensure transparent research attribution across all datasets, software releases, and publications in accordance with the CRediT taxonomy and DORA principles.
• Carry out other duties commensurate with the grade and remit of the post.

Person Specification

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

Criteria | Essential or desirable | Stage(s) assessed at

Hold a PhD in Chemical Engineering, Process Systems Engineering, Applied Mathematics, Computational Engineering, or a closely related discipline. (or equivalent relevant experience) | Essential | Application

Proven background in physics-informed machine learning, surrogate modelling, or hybrid data-driven/mechanistic modelling. | Essential | Application /Interview

Demonstrated experience in chemical reaction engineering, kinetic modelling, and reactor heat/mass transfer dynamics. | Essential | Application /Interview

Experience handling, cleaning, and structuring experimental or pilot-plant operational datasets for computational modelling. | Essential | Application /Interview

Effective communication skills, (both written and verbal), report writing skills, experience of delivering presentations. | Essential | Application /Interview

Ability to explain/present complex information to a range of audiences. | Essential | Application /Interview

Experience of working as part of a research group and of collaboration with academic and industrial partners. | Essential | Application /Interview

Well-developed analytical/problem solving capability to perform detailed analysis of information and identify issues to support decision making. | Essential | Application /Interview

Ability to plan and organise activities in a research project with respect to cost, timescales, resources and skills required | Essential | Application /Interview

Specific knowledge of Fischer-Tropsch (FT) synthesis, catalytic CO2 conversion, or gas-to-liquids processes. | Desirable | Application /Interview

Experience using commercial process simulation software (e.g., Siemens gPROMS) or Computational Fluid Dynamics (CFD) tools (e.g., ANSYS Fluent). | Desirable | Application /Interview

Further Information

Grade | 7

Salary | £39,560 - £41,885 per annum

Work arrangement | Full-time

Duration | Fixed-term to 11 July 2027

Line manager | Professor of Chemical Engineering Education

Right to work in the UK | If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the UK Visa & Immigration website.

Our website | Cmbe

For informal enquiries about this job contact Professor Mohammad Zandi, Professor of Chemical Engineering Education: on m.zandi@sheffield.ac.uk or on 0114 222 8265

Next steps in the recruitment process

The selection process will take place in late November/early December. We plan to let candidates know if they have progressed to the selection stage, which will comprise an interview, 2 weeks following the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact cmbe-staffing-contracts@sheffield.ac.uk.

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

• A minimum of 41days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways.
• A range of generous family-friendly policies
• paid time off for parenting and caring emergencies
• access to menopause support in the workplace
• paid time off and support for fertility treatment
• and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Leader (opens in a new window). If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.