PhD: Physics-Informed ML for Greenland Firn Tipping

DTU Space; Kongens Lyngby; On-site; DKK 330,000 - 370,000; Full time

Job summary

DTU Space at Lyngby Campus invites applications for a three-year PhD scholarship in Physics-Informed Machine Learning focused on Greenland’s firn tipping point. The project merges physics, ML, and satellite data to address climate questions within Europe’s Destination Earth framework.

You will work in a diverse, international team, learn scientific computing, and contribute to cutting-edge research. No prior glaciology experience is required; we provide training and supervision throughout the

Qualifications

• Master’s degree in physics or mathematics.
• Experience writing scientific code, from thesis or projects.
• Knowledge of differential equations, numerical methods, statistics or ML.
• Good written and spoken English.

Responsibilities

• Develop a physics-informed ML framework predicting when Greenland’s firn stops storing meltwater.
• Combine satellite-derived firn density with firn models (data assimilation).
• Develop machine-learning models that obey physical conservation laws.
• Detect early-warning signals of tipping points in future climate scenarios.
• Spend 3-6 months aboard visiting international partners, planned with you.
• Take part in teaching and knowledge-sharing as part of the DTU PhD program.

Skills

Scientific programming

English proficiency

Mathematical foundation

Differential equations

Numerical methods

Statistics

Machine learning

Education

Master’s degree in physics or mathematics

Two-year master’s degree (120 ECTS) or equivalent

Job description

DTU Space at Lyngby Campus invites applications for a three-year PhD scholarship in Physics-Informed Machine Learning focused on Greenland’s firn tipping point. The project merges physics, ML, and satellite data to address climate questions within Europe’s Destination Earth framework.

You will work in a diverse, international team, learn scientific computing, and contribute to cutting-edge research. No prior glaciology experience is required; we provide training and supervision throughout the