Overview
Mining operations worldwide generate significant water pollution, particularly from tailings dams that release metals like arsenic, copper, and selenium into rivers. These contaminants pose environmental challenges but can also be utilized as a resource for synthesizing new sorbents, presenting opportunities for innovative solutions in contaminated water treatment. This collaborative project between National Tsing Hua University (NTHU) and the University of Liverpool transforms waste metals into valuable sorbents for nutrient recovery, addressing both pollution and sustainable resource management.
About this opportunity
The research harnesses cutting-edge machine learning and experimental techniques to design and optimize new generation of sorbents. Advanced algorithms are developed to understand the interaction between metals and absorbents. Simultaneously, real-time experimental validation ensures the practical effectiveness of the sorbent materials. Working across two internationally leading institutions, the student will bridge the gap between environmental chemistry, machine learning, and materials design to enable sustainable water purification technologies. The student will play a central role in both experimental and computational aspects of the research. The responsibilities will include:
- Model development: Build and refine machine learning and thermodynamic models to predict metal recovery and sorbent efficiency
- Laboratory research: Conduct experiments to test ionic liquid-based metal recovery and to develop and evaluate sorbents
- Data analysis: Analyse results from lab and field-scale tests to validate models and optimize filter designs
- Collaboration and communication: Work closely with research teams contribute to meetings, present at conferences, and co-author publications
- Academic progress: Complete training, attend relevant workshops, and produce a final thesis.
Who is this opportunity for?
This project is open to UK and international applicants. Candidates will have, or be due to obtain, a master’s degree or equivalent from a reputable university in an appropriate field of Science and Engineering. Expertise in data science approaches and density functional theory is highly desirable.
We want all of our staff and students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.