Industrial Postdoctoral Researcher in Mathematical Analysis

Role Description

This is a full-time on-site role located in Lund for an Industrial Postdoctoral Researcher in Mathematical analysis. This Industrial Postdoctoral position is designed for a researcher with a deep, rigorous mathematical background who seeks to apply advanced theory to real-world challenges. The role centres on the mathematical foundations of satellite-based measurement and large-scale ocean observation, with a strong focus on analytical and numerical research.

The successful candidate will lead mathematically driven investigations involving:

• Mathematical analysis of satellite observation systems.
• Development of hybrid mathematical–AI models, with emphasis on underlying analytical structure and stability
• Design, analysis, and implementation of numerical and computational methods for large-scale geophysical and oceanographic data
• Mathematical modelling and interpretation of multi-source observational systems
• Your mathematical research will directly contribute to the conceptual design of next-generation surveillance systems and influence both scientific understanding and operational capabilities.

Key Responsibilities

• Conduct independent and collaborative research in mathematics, including PDEs, mathematical modelling, functional/harmonic analysis, or theoretical/mathematical physics
• Develop:
• Mathematically well-founded algorithms
• Methods for multi-source satellite data fusion
• Supervise Master’s students in mathematically focused research projects

Qualifications

Essential

• PhD in Mathematics
• Strong background in:
• Partial Differential Equations
• Algorithms Development
• Functional or Harmonic Analysis
• Theoretical Physics / Mathematical Modelling
• Proven experience using Python for scientific and numerical computing
• Evidence of mathematical research output (publications, preprints, algorithms, or open-source contributions)

Desirable

• Experience with C++, GPU acceleration, or high-performance computing.
• Familiarity with inverse problems.