Postdoctoral Researcher (Applied Mathematics, On-site)

Job summary

This position is for a 2-year fixed-term postdoctoral role focused on uncertainty-aware optimization in inverse problems related to 3D scanning technologies. The successful candidate will engage in research that combines numerical linear algebra, optimization, and computational imaging to enhance the reliability of reconstructed 3D models by quantifying and propagating measurement uncertainty.

Qualifications

• PhD in Mathematics, Statistics, or a related field, awarded after March 2021 or expected by November 2026.
• Strong expertise in numerical linear algebra, optimization, and inverse problems.
• Solid understanding of linear least squares methods and iterative solvers, particularly Krylov subspace methods.
• Experience or strong interest in uncertainty quantification and statistical modeling.
• Proficient in at least one programming language (preferably two) such as C#, C++, or Python.
• Familiarity with machine learning methods is advantageous.
• Ability to work independently and collaboratively in an industrial-academic environment.
• Strong communication skills for presenting complex ideas clearly.

Responsibilities

• Develop methods for characterizing measurement uncertainty in medical 3D scanning data.
• Work with numerical algorithms, including iterative methods and related solvers.
• Analyze and develop new methods for uncertainty-aware linear least squares optimization.
• Design mathematical frameworks for quantifying uncertainty in optimization algorithms.
• Contribute to the design of uncertainty-aware components in existing reconstruction pipelines.
• Translate theoretical models into scalable implementations for industrial use.
• Conduct simulation studies to validate theoretical developments.
• Collaborate with researchers and engineers to integrate new methods into production systems.
• Contribute to scientific publications and presentations.

Skills

• Expertise in numerical linear algebra and optimization techniques.
• Programming skills in C#, C++, or Python.
• Familiarity with machine learning methods.

Education

• PhD in Mathematics, Statistics, or a related discipline.

Tools

• Numerical algorithms, programming languages (C#, C++, Python).