Are you passionate about pushing the boundaries of how we model physics of the Earth system with deep learning models? Recent breakthroughs in foundation models have shown remarkable capabilities, from simulating atmospheric dynamics, to generating spectral components of satellite data, and even accelerating power flow simulations in electric grids. Yet, a critical question remains: How physically grounded and robust are these models in real-world scientific and engineering applications?

This thesis will focus on advancing the physical representation capabilities of geospatial foundation models. Despite their growing use, these models still face key challenges

• How well can they extrapolate to extreme events?
• When do they hallucinate or produce physically impossible outputs?
• What methodologies can be implemented to detect and mitigate such failures, especially in domains where reliability and scientific validity are essential?

You will explore these questions by developing and evaluating approaches to improve physical output, robustness, and trustworthiness in foundation models for geospatial and Earth system applications.

Key Research Activities

• Enhance physical system representation within foundation models
• Curate and analyze diverse, high-quality pretraining datasets
• Investigate robust, scalable model architectures
• Analyze internal representations and physical consistency
• Develop validation protocols for robustness and generalization
• Design and implement guardrails to detect and mitigate hallucinations
• Apply techniques to real-world geospatial models in collaboration with domain experts
  
  

Why This Opportunity?

As part of IBM Research – Zurich, you’ll join a world-class team of scientists and engineers in a dynamic, interdisciplinary environment. You will gain hands-on experience with large-scale AI systems, collaborate with leading organizations like NASA and ESA, contribute to open-source tools and HuggingFace models, and publish your findings in top-tier venues.

Required Skills

• Excellent academic track record in Computer Science, Data Science, Statistics, Scientific Computing, Applied Mathematics, or related fields
• Very strong background in deep learning
• Proficiency in collaborative coding environments (e.g., GitHub, GitLab)
• Strong problem-solving skills and ability to synthesize research into novel deep learning architectures
• Enthusiasm for robust, responsible AI in scientific applications
  
  

Preferred Qualifications

• Experience with large-scale training on GPU clusters (e.g., Slurm)
• Understanding of physical processes in the Earth system or in energy systems
• Experience with large geospatial datasets and scientific data formats
• Passion for open science
  
  

Diversity

IBM is committed to

diversity

at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

Please submit your application through the link below.