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 innovative machine learning models combined with Raman spectroscopy to transform the detection of hazardous crystalline silica particles. Lead advancements in occupational health through smart-data processing and automated spectral analysis.
AI-generated overview
Respirable Crystalline Silica
Raman Spectroscopy
Machine Learning
Data Processing
Occupational Health
Environmental Safety
Project Description
This PhD project addresses a major occupational health challenge: exposure to respirable crystalline silica (RCS), which can cause serious diseases such as silicosis and lung cancer.
The research aims to develop an advanced data processing pipeline using:
Raman spectroscopy for improved detection
machine learning for automated analysis
Monte Carlo simulations for generating robust datasets
The project will:
improve detection of nano-sized silica particles
develop advanced spectral analysis workflows
integrate ML models for accurate quantification
validate methods using laboratory and real-world samples
The work includes collaboration with:
UK Health and Safety Executive (HSE)
Stockholm University
The candidate will gain experience in:
Raman spectroscopy, XRD, FTIR
data processing and computational modelling
occupational health research
This project is part of a Graduate Teaching Assistantship (GTA), including up to 180 hours ofThis PhD project addresses a major occupational health challenge: exposure to respirable crystalline silica (RCS), which can cause serious diseases such as silicosis and lung cancer.
The research aims to develop an advanced data processing pipeline using:
Raman spectroscopy for improved detection
machine learning for automated analysis
Monte Carlo simulations for generating robust datasets
The project will:
improve detection of nano-sized silica particles
develop advanced spectral analysis workflows
integrate ML models for accurate quantification
validate methods using laboratory and real-world samples
The work includes collaboration with:
UK Health and Safety Executive (HSE)
Stockholm University
The candidate will gain experience in:
Raman spectroscopy, XRD, FTIR
data processing and computational modelling
occupational health research
This project is part of a Graduate Teaching Assistantship (GTA), including up to 180 hours of teaching/research support annually.
Entry Requirements
1st or 2:1 Honours degree in:
Physics
Materials Science
Computer Science
or related discipline
Desirable:
experience in:
chemometrics
machine learning
data modelling
English requirement (international):
IELTS 7.0 overall
minimum 6.5 per component
Physics
Materials Science
Computer Science
or related discipline
Desirable:
experience in:
chemometrics
machine learning
data modelling
English requirement (international):
IELTS 7.0 overall
minimum 6.5 per component
How to Apply
Apply via Sheffield Hallam University online application form
Submit:
Personal statement (max 2 pages)
Two references or referee details
Degree certificate
IELTS + passport (if applicable)
Application deadline: 07 May 2026
Interviews: TBC
Submit:
Personal statement (max 2 pages)
Two references or referee details
Degree certificate
IELTS + passport (if applicable)
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 leads research integrating advanced analytical techniques with machine learning to solve real-world occupational health problems. His work focuses on developing innovative spectroscopy methods for hazardous material detection. He collaborates with international health and safety bodies to ensure impact in workplace safety regulations.
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