Research Associate for AI-Enabled Process Optimization and Resource Recovery

Location: Imperial South Kensington Campus

About the role:

Join the cutting-edge R3VOLUTION and Cardimed projects, two game-changing projects aiming to enable over 90% water reuse in water-intensive industries, recover more than 45% of effluent solutes, reuse over 50% of waste heat, and eliminate 100% of hazardous substances. As the Integration Lead for an AI-based Digital Process Assistant (DPA), you will spearhead the deployment of a real-time digital twin at multiple pilot sites in the petrochemical, bio-based chemical, pulp and paper, and steel industries. Collaborating closely with diverse project partners, you will ensure that the DPA seamlessly connects to live sensor data, runs simulations to fine-tune process variables, and provides intelligent recommendations to optimize water usage, resource recovery, and system performance.

What you would be doing:

You will define and implement robust data retrieval, storage, and processing frameworks that ensure compatibility with existing monitoring and control systems. You will feed real-time sensor data into the digital assistant to facilitate simulations and generate process recommendations. You will conduct offline simulations to validate and calibrate Machine Learning models using experimental data from physical demonstration sites. You will support iterative testing and re-training of these models in operational environments by monitoring performance against key indicators. You will oversee the live operation of the digital assistant, refining its parameters to meet project targets. Finally, you will collaborate with membrane technology experts, process engineers, and data scientists to continuously improve the solution and share findings through technical reports and publications.

What we are looking for:

• Hold a PhD (or near completion) or equivalent experience in Computer Science, Engineering, Applied Mathematics, Environmental Science (with strong computational focus), or a related field.
• Proficiency in one or more programming languages (e.g., Python, R, C++).
• Experience with data engineering – including real-time sensor data integration, data pipelines, and database solutions
• Experience of establishing robust data quality protocols, including real-time data validation at the point of capture, handling missing or noisy data, and implementing continuous monitoring strategies to ensure data integrity
• Proven experience of digital twin concepts, including their design, implementation, and operational integration in industrial contexts
• Ability to synthesize complex environmental data and derive meaningful insights that can inform project decisions and strategies.

What we can offer you:

• The opportunity to work on cutting-edge EU-funded projects, driving sustainable innovation in industrial processes.
• Collaboration with multidisciplinary teams at Imperial College London, renowned for leading innovation in sustainability and resource efficiency.
• Development of project management and leadership skills through active involvement in high-profile projects, working with cross-sectoral partners, and managing deliverables.
• Hands-on experience with advanced digital technologies, contributing to innovative solutions for a resilient future.
• Professional development opportunities, including publishing in leading journals, presenting at international conferences, and shaping industry standards in sustainability and resilience.
• Access to a sector-leading salary and benefits package, extensive training programs, and career development resources to support your professional goals.

Further Information

Please refer to the job description for full details and essential requirements for the post.

This is a fixed term post for 1 year.

For informal enquiries about the post please contact Professor Evina Katsou on:

e.katsou@imperial.ac.uk.

For queries regarding the recruitment process please contact Briony Webb on:

b.webb@imperial.ac.uk.

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.

Advert information