Digital Twin Lead: Fischer-Tropsch Reactors (DigiTwin-FT)

Dunhillmedical; Sheffield; On-site; GBP 40,000 - 42,000; Full time

Benefits offered by this job

41 days annual leave (pro rata) and購se

Hybrid working opportunities

Generous pension scheme

Discounts and rewards on shopping, etc

Development and wellbeing support

Job summary

University of Sheffield is seeking a Research Associate to develop and validate a predictive digital twin for Fischer-Tropsch reactors under the DigiTwin-FT project. You will leverage pilot-scale data, integrate physics-informed models, and collaborate with Siemens to align with industrial workflows.

The role involves TEA and LCA scoping, data engineering, manuscript preparation, and contributing to IP and grant proposals. A PhD in a related field is essential.

Qualifications

• PhD in Chemical Engineering, Process Systems Engineering, Applied Mathematics, Computational Engineering, or closely related field.
• Proven background in physics-informed machine learning, surrogate modelling, or hybrid data-driven/mechanistic modelling.
• Demonstrated experience in chemical reaction engineering, kinetic modelling, and reactor heat/mass transfer dynamics.
• Experience handling, cleaning, and structuring experimental or pilot-plant datasets for computational modelling.
• Effective communication, report writing and presentations.

Responsibilities

• Lead extraction, cleaning, and structuring of historical and live datasets from pilot-scale FT reactor.
• Develop, calibrate, and validate hybrid models integrating multi-phase fluid dynamics, reaction kinetics, and heat/mass transfer.
• Computationally extrapolate the pilot-scale twin to commercial conditions, map hotspots and bottlenecks.
• Execute early-stage TEA and LCA scoping models using simulated outputs to evaluate costs and carbon metrics.
• Drive day-to-day delivery of Work Packages and ensure adherence to timelines.
• Maintain clean, well-documented, version-controlled code repositories and reproducible data pipelines.
• Collaborate with industrial partners and Siemens to align digital twin architectures with engineering workflows.
• Coordinate knowledge exchange across GBC community and document methodologies per guidelines.
• Lead drafting of high-quality research manuscripts for peer-reviewed journals.
• Assist with co-created follow-on proposals leveraging the digital baseline.
• Embed RRI and ED&I principles in activities and ensure transparent attribution per CRediT/DORA.
• Carry out other duties commensurate with the grade.

Skills

Physics-informed ML

Surrogate modelling

Hybrid data-driven modelling

Process modelling

Digital twins

Chemical reaction engineering

Education

PhD in Chemical Engineering or closely related discipline

Tools

Siemens gPROMS

CFD tools (ANSYS Fluent)

Job description

University of Sheffield is seeking a Research Associate to develop and validate a predictive digital twin for Fischer-Tropsch reactors under the DigiTwin-FT project. You will leverage pilot-scale data, integrate physics-informed models, and collaborate with Siemens to align with industrial workflows.

The role involves TEA and LCA scoping, data engineering, manuscript preparation, and contributing to IP and grant proposals. A PhD in a related field is essential.