Research Fellow (Dept of the Built Environment)

Interested applicants are invited to apply directly at the

Your application will be processed only if you apply via

We regret that only shortlisted candidates will be notified.

Job Description

. Conduct research to improve understanding and forecasting of extreme convective hazards in the Maritime Continent.
. Review the literature on maritime climate modelling
. Analyse convective storms with emphasis on convection initiation, using multi-source observational datasets, remote sensing products, and reanalysis.
. Integrate and compare observations, NWP model outputs, and nowcasting model products for hazard prediction workflows.
. Develop and apply machine learning-based post-processing methods to enhance forecast skill for convective hazards.
. Perform spatio-temporal data analytics on weather and/or hydrological fields.
. Collaborate closely with project team members contribute to end-to-end research activities and shared datasets/tools.
. Lead and contribute to peer-reviewed publications, conference presentations, and internal research reporting.
. Maintain organized research workflows (version control, reproducible experiments, documentation).
. Support broader project goals as needed (e.g., dataset curation, method benchmarking, cross-validation with partners).

Job Requirements

. Ph.D. in Atmospheric Science, Meteorology, Hydrology, Machine Learning, Physics, or related discipline (completed or expected within 2-3 months).
. Demonstrated experience with spatio-temporal data analytics for weather and/or hydrological fields.
. Proven machine learning experience (e.g., post-processing, prediction, classification/regression, uncertainty estimation, model evaluation).
. Familiarity with remote sensing products and reanalysis datasets ability to integrate multi-source observational data.
. Strong programming skills
. Good publication record in relevant journals.
. Strong written and oral communication skills.
. Highly organized, proactive, and able to work both independently and collaboratively in a multidisciplinary team.
. Proficient command of spoken and written English.
. Experience working with or evaluating NWP outputs and/or nowcasting systems is advantageous.
. Prior experience in international research collaborations is a plus.