Project Overview
The University of Sheffield, funded by the EPSRC and in collaboration with Shell, is offering a fully funded 4-year PhD studentship through the EPSRC Industrial Doctorate Landscape Award (IDLA). This research aims to investigate natural source zone depletion (NSZD) processes that can naturally remedy oil pollution and use this improved process understanding to enhance CoronaScreen, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will strengthen sustainable groundwater management, align with the SuRF-UK Sustainable Remediation framework, and support the UK’s environmental policies, including decarbonization efforts. The PhD student will be co-supervised by both academic and industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modeling.
Research Objectives
This project will focus on:
- Understanding NSZD processes and their relative importance in LNAPL depletion.
- Integrating NSZD processes into CoronaScreen for broader contaminant modeling.
- Developing advanced transport models incorporating time-dependent source depletion.
- Reducing uncertainty in groundwater risk assessments through refined numerical methods.
- Applying the improved model to real-world groundwater contamination case studies.
Career Development & Industry Collaboration
This PhD offers exceptional career development opportunities, including:
- Direct collaboration with scientists and environmental managers at Shell.
- Expert co-supervision from the University of Sheffield’s Groundwater Protection & Restoration Group and Shell’s Global Soil & Groundwater Solutions Team.
- A 3-month placement at Shell (in Year 3) for hands-on experience in environmental management.
- Interdisciplinary training in hydrogeology, contaminant transport modeling, and decision-making.
- Networking & knowledge exchange with leading academic and industry professionals.
Career Pathways
Graduates will develop highly transferable skills, preparing them for careers in:
- Academia (postdoctoral research at universities and research institutes).
- Industry (energy sector, environmental consultancy).
- Policy & Regulation (government bodies, environmental regulators and management organizations).
Travel & International Exposure
- Fully funded national & international conference attendance.
- Potential research visits to academic and industry partners.
Training & Skill Development
The student will receive specialized, multi-disciplinary training, in:
- Groundwater contaminant transport modeling & environmental risk assessment.
- Numerical simulation techniques for hydrogeological systems.
- Advanced uncertainty quantification for robust modeling.
- Scientific communication, including publications & conference presentations.
- Industry best practices through Shell collaboration.
Candidate Requirements
We seek a highly motivated candidate with:
- A First-Class or Upper Second-Class degree (or equivalent) in Civil Engineering, Environmental Engineering, Hydrogeology, Geosciences, Environmental Sciences, or related STEM disciplines (e.g., Applied Mathematics, Physics, Computational Sciences).
- Experience in numerical modeling and data analysis.
- An interest in groundwater contamination, risk assessment, and sustainability.
- Programming experience (Python, MATLAB, or similar) is desirable but not essential. However, applicants should be eager to develop strong coding skills as part of the project.
- Ability to work both independently and collaboratively in academic and industrial settings.
How to Apply
Submit the following: CV, a Cover letter outlining suitability, Academic transcripts, Two referees’ contact details
Contact Dr. Domenico Baù (d.bau@sheffield.ac.uk) for inquiries.
Funding
Fully funded tuition fee and stipend. International applicants are welcome to apply but should be aware that the recruitment process is highly competitive as UKRI limits the number of International candidates to a maximum of 30% per cohort.
