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Kok Siew Ng

4 days ago

Fully Funded PhD in Environmental Fate and Impacts of Electronic Waste Brunel University of London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD opportunity under a NERC TREES Doctoral Landscape Award (DLA) for 2027.

Deadline

Dec 14, 2026

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Country

United Kingdom

University

Brunel University London

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Keywords

Computer Science
Data Science
Chemistry
Environmental Science
Chemical Engineering
Materials Science
Circular Economy
Technical Engineering
Physics
ML

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About this position

Fully funded PhD opportunity at Brunel University of London within a NERC TREES Doctoral Landscape Award (DLA) for 2027.

The project is titled A Systems Approach to Understanding the Environmental Fate and Impacts of Electronic Waste and focuses on the environmental fate and impacts of electronic waste, with strong links to Environmental Science, Chemical Engineering, Physics, Chemistry, and data-driven methods such as machine learning and data science.

The PhD is interdisciplinary and includes three main strands: sustainability assessment supervised by Dr Kok Siew Ng, experimental validation co-supervised by Dr Abhishek Lahiri, and data science/machine learning co-supervised by Dr Yang Yang.

Eligibility highlights: this call is for a UK Home student with a First Class or Upper Second Class degree and a relevant background in Engineering, Environmental Science, Physics, or Chemistry.

Funding: the post is described as fully funded under the NERC TREES DLA.

Deadline: 5pm UK time, Monday 14 December 2026. More information about the application process and eligibility criteria is available via the linked TREES DLA page.

Funding details

Fully funded PhD opportunity under a NERC TREES Doctoral Landscape Award (DLA) for 2027.

What's required

UK Home student status is required. Applicants should have a First Class or Upper Second Class degree and a background in Engineering, Environmental Science, Physics, or Chemistry. The project is interdisciplinary and involves sustainability assessment, experimental validation, and data science/machine learning.

How to apply

Read the application and eligibility details on the TREES DLA page, then follow the application process linked in the post. Submit before 5pm UK time on Monday 14 December 2026.

More information can be found here

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