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UCL

AI-Powered Life Cycle Assessment for Emerging Clean Technologies

University College London Department of Chemical Engineering
✓ Fully Funded machine learning sustainability life cycle assessment environmental engineering ai batteries clean technology solar cells

Develop AI-driven Life Cycle Assessments to automate and accelerate environmental impact evaluations of emerging clean technologies. Transform sustainability feedback processes to enable timely and reliable assessments.

AI-generated overview

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Why This Research Matters

This research revolutionizes how environmental assessments are conducted, enabling faster and more consistent sustainability evaluations that support the responsible development of emerging clean technologies. By integrating AI into LCA, it helps bridge knowledge gaps and accelerate the global transition to sustainable energy solutions.

Artificial Intelligence Life Cycle Assessment Sustainability Science Clean Technology Environmental Modelling Energy Innovation

Project Description

This project develops an innovative AI-driven Life Cycle Assessment (LCA) approach for emerging clean technologies. It bridges sustainability science, AI, and materials and energy innovation aiming to transform environmental assessments for rapidly evolving technologies. Work with supervisors Dr Andrea Paulillo and Dr Eike Cramer and collaborate with research groups to create real world case studies. Develop AI tools that automate inventory data collection and refinement to streamline LCA modeling processes. Deliver integrated AI workflows improving speed, consistency, reproducibility, and quality of LCAs vs traditional approaches. Gain hands-on experience with cutting-edge clean tech like next generation solar cells and batteries. Accelerating sustainability feedback loops for early-stage technologies helps ensure their environmentally responsible development and supports cleaner future energy solutions globally.

Entry Requirements

Applicants should have or soon complete an upper second-class or higher degree at MEng or MSc level in chemical/environmental engineering, physics, computer science, or related quantitative field. Experience in LCA or machine learning/AI is desirable. Must be able to work independently and collaboratively.

How to Apply

Apply via https://evision.ucl.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RRDCENSING01&code2=0041. Nominate Dr. Andrea Paulillo as supervisor and include a statement of interest. For enquiries contact andrea.paulillo@ucl.ac.uk.

Eligibility

UK/Home
EU
International

Supervisor Profile

DA
Dr Andrea Paulillo
University College London, Department of Chemical Engineering
Google Scholar

Dr Andrea Paulillo specializes in chemical engineering with a focus on integrating AI techniques to enhance environmental modelling. His research bridges sustainability science and energy innovation, pioneering novel methods to accelerate Life Cycle Assessments. He collaborates broadly to develop practical AI tools for emerging clean technologies.