PhD scholarship in Physics-Informed Machine Learning for Greenland’s Firn Tipping Point - DTU Space

Technical University of Denmark · Kongens Lyngby

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Are you interested in turning natural phenomena into equations and code, and then applying them to big open questions in climate science? If so, we are looking for a PhD student with a background in physics and/or mathematics to help predict when Greenland’s porous near-surface firn layer, which currently stores much of the ice sheet’s meltwater, loses that capacity, and what that means for sea-level rise.

You will build models that combine physics, machine learning, and satellite data, and apply them to climate scenarios from the EU’s Destination Earth initiative. You will join a diverse and collaborative group at DTU Space and develop skills in scientific computing and machine learning that are valued in both research and industry. No prior experience in glaciology or climate science is needed; we will help you learn the field.

Your main tasks will be to:

To do well in the role, we think you need:

Experience with glaciology, remote sensing, or machine learning is not required.

You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.

The period of employment is 3 years.

You can read more about career paths at DTU here.

You can read more about our DTU Space cryosphere research at https://www.space.dtu.dk/english/research-divisions/geodesy-and-earth-observation/research-areas/cryosphere

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. 

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it. 

Applications received after the deadline will not be considered.

All interested candidates, irrespective of age, gender, disability, race, religion or ethnic background, are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position.

You will join the Cryosphere Research Group in the Geodesy and Earth Observation Division at DTU Space, Lyngby Campus. We use satellites, models and on-the-ground measurements to understand how ice sheets and glaciers are changing and what this means for sea level, in projects for ESA and the EU. Our group is international and collaborative, and we value curiosity, good supervision and a healthy work-life balance. 

DTU – For the benefit of society since 1829
DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.

Turn physics into code for climate science: in this 3-year PhD at DTU Space, you will combine physics-informed machine learning and satellite data to predict when Greenland’s firn loses its meltwater storage capacity.

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