SHU
Development of A Smart-Data Processing Pipeline for Quantification of Respirable Crystalline Silica
✓ Funded (Competition)
⏰ Closing Soon
machine learning
analytical chemistry
materials science
environmental engineering
artificial intelligence
environmental chemistry
environmental sciences
Develop cutting-edge AI-enhanced spectral analysis methods for occupational health monitoring. Create innovative machine learning models that enable precise detection of hazardous crystalline silica nanoparticles.
AI-generated overview
Respirable Crystalline Silica
Raman Spectroscopy
Machine Learning
Occupational Health
Data Modelling
Environmental Safety
Project Description
This PhD project addresses a major occupational health challenge by developing an advanced smart-data processing pipeline for the detection and quantification of respirable crystalline silica (RCS).
RCS is generated during industrial activities such as cutting, grinding, and polishing materials like stone, bricks, and tiles. Exposure to fine silica dust is linked to serious diseases including silicosis, lung cancer, and autoimmune disorders. Current monitoring methods such as XRD and FTIR have limitations, especially in detecting nano-sized particles.
The project will develop a Raman spectroscopy-based approach and combine it with machine learning to improve sensitivity, automate spectral pattern recognition, and strengthen quantitative analysis.
The research will involve:
refining spectral analysis workflows
applying machine learning for detection and classification
using Monte Carlo simulations to generate datasets
validating the method with laboratory experiments using real and simulated samples
collaborating with the UK Health and Safety Executive and Stockholm University
The project is part of a Graduate Teaching Assistantship, so the successful candidate will also contribute up to 180 hours of teaching or research support activity each academic year.
Entry Requirements
At least a 1st or 2:1 Honours degree in:
Physics
Materials Science
Computer Science
or a related discipline
Desirable:
background in chemometrics
data modelling
machine learning
For international applicants:
IELTS 7 overall
minimum 6.5 in all components
or equivalent taken within the last two years
Physics
Materials Science
Computer Science
or a related discipline
Desirable:
background in chemometrics
data modelling
machine learning
For international applicants:
IELTS 7 overall
minimum 6.5 in all components
or equivalent taken within the last two years
How to Apply
Apply through the Sheffield Hallam University online application form.
Upload:
Personal statement, up to 2 pages
Two letters of reference, or details of two referees
Copy of highest degree certificate
For non-UK applicants, IELTS or equivalent and passport copy
If applying for multiple GTA projects, list them clearly and submit a tailored personal statement for each.
Application deadline: 07 May 2026
Interviews: TBC
Upload:
Personal statement, up to 2 pages
Two letters of reference, or details of two referees
Copy of highest degree certificate
For non-UK applicants, IELTS or equivalent and passport copy
If applying for multiple GTA projects, list them clearly and submit a tailored personal statement for each.
Application deadline: 07 May 2026
Interviews: TBC
Eligibility
UK/Home
EU
International
Supervisor Profile
DR
Dr Ronak Janani
Sheffield Hallam University, Engineering and Built Environment
Dr Ronak Janani’s research focuses on integrating advanced analytical techniques with computational methods to improve health-related environmental monitoring. His work involves Raman spectroscopy, data modelling, and machine learning to tackle industrial pollutant detection challenges. He collaborates internationally and emphasizes practical solutions with regulatory impact.
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