Work on the UK NERC-funded grant “Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”.
Use novel machine learning-based methods to determine the climate response to a range of natural events.
Collaborate closely with a Scientist at ETH Zurich to apply a new Neural Network-Based Cloud Fraction scheme into UKESM.
Design sensitivity experiments to test its effect and lead further improvement of cloud fraction scheme for climate simulations.

Requirements

PhD in a highly quantitative subject, such as, but not limited to, atmospheric or climate science, meteorology, physics, earth observation.
Expertise in working with climate modelling, ideally experience with model development with FORTRAN.
Good programming skills in a data processing and visualization language such as Python, MATLAB, R or NCL.
Proven ability to work both independently and as part of a team.
Experience of disseminating findings through scientific publications and conference presentations.

Benefits

A competitive salary.
An exciting, positive, creative, challenging and rewarding place to work.
To be part of a diverse and vibrant international community.
Comprehensive Staff Benefits, including generous annual leave entitlement, a defined benefits pension scheme, a wide range of staff discounts, family-friendly initiatives, and flexible work options.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningclimate modellingFORTRANPythonMATLABRNCLdata processingdata visualizationcloud fraction scheme

Soft Skills

independent workteam collaborationcommunicationdissemination of findings

Certifications

PhD