PhD Candidate: Computational Techniques for Filling Data Gaps in Life Cycle Assessment

Are you interested in exploring, developing and applying new computational techniques to fill environmental sustainability data gaps? Then join our environmental science group for a 4-year PhD position focused on filling gaps in life cycle inventory data to improve the quantification of product environmental footprints. You will be part of an international team dedicated to solving environmental sustainability challenges in a collaborative project between Radboud University and Unilever’s Safety and Environmental Assurance Centre.

Life cycle inventory (LCI) data represent the material and energy requirements as well as emissions of production processes, forming the backbone of environmental life cycle assessments (LCAs). However, there remains a wide gap between the diversity of real-world production systems (and associated processes) and LCI data available to represent them. You will explore, develop, and apply computational techniques, including machine-learning approaches, to fill data gaps. Gaps can be filled by extrapolating requirements of production processes across similar locations or extrapolating production processes across similar products. The focus will be on agricultural production systems. This project includes data compilation for material and energy requirements and emissions, the development of computational techniques to generate new LCI data, and an application to quantify product footprints and evaluate the implications of using LCI databases enhanced by imputed data. It is expected that 10% of the workload will be spent on education.