Qualification Type: | PhD |
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Location: | Sheffield |
Funding for: | UK Students, EU Students |
Funding amount: | Not Specified |
Hours: | Full Time |
Placed On: | 12th March 2025 |
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Closes: | 31st March 2025 |
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:
Career Development & Industry Collaboration
This PhD offers exceptional career development opportunities, including:
Career Pathways
Graduates will develop highly transferable skills, preparing them for careers in:
Travel & International Exposure
Training & Skill Development
The student will receive specialized, multi-disciplinary training, in:
Candidate Requirements
We seek a highly motivated candidate with:
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.
Type / Role:
Subject Area(s):
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