Jobid=e37dc2c9fc63 (0.0156)
PhD Position in Dynamics-Aware Downscaling of Extremes over Arctic Glaciers
Extreme Arctic glacier melt is driven by multi-scale compound drivers. We will leverage machine learning methods to bridge the gap between drivers at coarse model resolutions and impacts captured by high-resolution observations.
Arctic glacier melt drives regional water availability, glacial outburst flood risks, freshwater input to the ocean, and global sea‑level rise. A growing body of evidence shows that a substantial portion of glacier runoff results not from gradual warming, but from short‑lived, intense melt and precipitation events. These events are fueled by global‑scale atmospheric drivers and amplified by local topographic conditions. Current global climate models (GCMs) lack the spatial resolution to capture these processes, while high‑resolution regional models remain too computationally expensive for large‑ensemble use. Frequently, even higher resolutions do not capture dynamic drivers. This mismatch limits the utility of climate projections for predicting localized glacier change. This project aims to close this gap by developing a machine‑learning‑based downscaling framework that links coarse resolution (0.25°–1°) reanalysis and climate model outputs to fine‑scale (~100 m) estimates of surface melt and precipitation based on observations across Arctic glaciers, particularly the drivers and impacts of extreme events.
As a PhD student at TU Delft, you will work on Arctic science at the nexus of models, AI, and spaceborne remote sensing. You will first identify large‑scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks and diffusion models, to estimate high‑resolution melt and precipitation fields. This emulator will be used to drive surface mass balance and glacier models, substantially expanding the impact of climate simulations. This work will quantify the contribution of extreme events to glacier runoff and mass loss, improve predictions of future glacier change under multiple climate pathways, and provide open‑source tools for broader application. The project will advance scientific understanding of cryosphere‑atmosphere interactions and support societal adaptation through improved projections of Arctic glacier behaviour under climate change.
Your project will be conducted within the Geoscience and Remote Sensing Department at TU Delft with multiple collaborators on‑site, interacting with a vibrant community on the TU Delft campus. You will also have regular contact with collaborators at Vrije Universiteit Brussels (including one extended trip) and the National Center for Atmospheric Research (NCAR) in the US. Our group at TU Delft is dedicated to building a collaborative, supportive environment that will help you flourish both personally and professionally.
Job requirements
- You hold an MSc in Earth science, environmental science, data science, physics, mathematics or computer science, with practical machine learning/artificial intelligence courses and relevant project and thesis experience.
- You have a keen interest in or experience working in the domain of the cryosphere, and interact with the larger science community.
- You are intrinsically excited about the prospect of creative collaborative science.
- You are motivated to develop expertise independently with multiple tools, including observations and models, AI and statistical methods.
- You have a good command of written and spoken English, as scientific work will be conducted in English.
Challenge. Change. Impact!
Additional information
For more information about this vacancy, please contact Rajashree (Tri) Datta, Assistant Professor at
Application procedure
Are you interested in this vacancy? Please apply no later than 14 April 2026 via the application button and upload the following documents:
- A detailed CV.
- Cover letter.
- An abstract of the Master thesis (1 page).
- Copies of MSc manuscripts and course/grade transcripts (in English).
You can address your application to Rajashree (Tri) Datta.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English‑taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
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