Technical University of Denmark
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DTU PhD scholarship
PhD Scholarship in Physics-Informed Machine Learning for Greenland’s Firn Tipping Point Technical University of Denmark in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
This is a full-time, three-year PhD scholarship and employment appointment at DTU. Salary and appointment terms follow the collective agreement with the Danish Confederation of Professional Associations; the specific salary amount is not stated.
Deadline
Nov 29, 2026
Country
Denmark
University
Technical University of Denmark
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About this position
The Technical University of Denmark (DTU) Space invites applications for a three-year, full-time PhD scholarship on physics-informed machine learning for Greenland’s firn tipping point. The position is based at the Cryosphere Research Group, Geodesy and Earth Observation Division, DTU Space, in Kgs. Lyngby, Denmark.
The project will investigate when Greenland’s porous near-surface firn layer may lose its capacity to store meltwater and what this could mean for sea-level rise. The PhD researcher will combine physics-based models, machine learning, satellite observations, and future climate scenarios from the EU Destination Earth initiative. Research tasks include identifying climate controls on firn meltwater absorption, assimilating satellite-derived firn density into firn models, developing physically constrained machine-learning models such as graph neural networks, and detecting early-warning signals of climate tipping points.
Applicants should hold, or be close to completing, a two-year master’s degree equivalent to 120 ECTS in physics, mathematics, or a related quantitatively rigorous field. Experience with scientific programming and knowledge of differential equations, numerical methods, statistics, or machine learning are expected. Previous glaciology, remote sensing, or climate-science experience is not required. English proficiency is required.
The appointment is full-time for three years, with salary and terms governed by the Danish Confederation of Professional Associations agreement. The start date is 1 February or soon thereafter. The research includes a planned 3–6-month visit to international partner institutions. Applications must be submitted by 29 November 2026 at 23:59 Danish time.
Submit one PDF in English containing a motivation letter, CV, academic transcripts and diplomas with grading-scale information, and a one-page description of a computational or mathematical project. Assessment will be conducted by Sebastian B. Simonsen, Kirk M. Scanlan, and Anja Rutishauser, and shortlisted applicants may be invited to a structured interview and computational exercise.
Funding details
This is a full-time, three-year PhD scholarship and employment appointment at DTU. Salary and appointment terms follow the collective agreement with the Danish Confederation of Professional Associations; the specific salary amount is not stated.
What's required
Applicants must have a two-year master's degree equivalent to 120 ECTS in physics, mathematics, or a field with a comparable mathematical foundation. Applicants should have experience writing scientific code and good knowledge of at least one of differential equations, numerical methods, statistics, or machine learning, as well as good written and spoken English. Experience in glaciology, remote sensing, or machine learning is not required. Applicants may apply before completing the master's degree but must obtain it before starting. Application materials must be submitted in English in one PDF containing a motivation letter, CV, grade transcripts and BSc/MSc diploma with grading-scale description, and a one-page description of a computational or mathematical project. Shortlisted candidates may complete a structured interview and short computational exercise.
How to apply
Submit the complete online application through the DTU Apply Now portal by 29 November 2026 at 23:59 Danish time. Upload all required materials in English as one PDF file. Applicants should include a cover letter, CV, academic transcripts and diplomas, and a one-page computational or mathematical project description.
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