We are seeking a Senior Associate with expertise in atmospheric chemistry and air quality measurements, and an interest in translating research findings into insights that support air quality policy. The candidate should have experience in air quality measurements using reference-grade instruments, such as beta attenuation monitors and gas analysers; trend analysis of air quality data using reference-grade instruments and/or low-cost sensors; or deploying and analysing data from large networks of low-cost sensors to characterise urban and regional air pollution. Knowledge of the principles and operation of reference-grade instruments and low-cost sensors, and experience using AMS, ACSM, or similar higher-end instruments will be an advantage. Familiarity with air quality management will be a plus.
Responsibilities
- Conduct air pollution studies using advanced instrumentation, reference instruments, reference- or research-grade gas monitors, and networks of low-cost air quality sensors, and analyse the resulting data.
- Guide the development of data analysis tools and packages, as needed, in collaboration with the AI and Digital Platforms team.
- Participate in and organise workshops, and deliver lectures as part of capacity-building programmes for external stakeholders, including government officials and non-specialists.
- Contribute to external funding proposals and prepare reports and peer-reviewed manuscripts.
- Mentor junior researchers and present research findings at workshops and conferences.
Qualifications
PhD in engineering or sciences, with doctoral research in air pollution, atmospheric chemistry, or a related field.
Skill Set
- Expertise in operating and maintaining AMS/ACSM/similar research instruments and/or large networks of low-cost air quality sensors.
- Proficiency in Python, R, or Igor Pro for data analysis and visualisation.
- Excellent oral and written communication skills, demonstrated through peer-reviewed publications in high-quality journals and presentations at international conferences.
Duration
24 months
