The Scientific Computing Center (SCC) is a central scientific institution of KIT in connection with tasks in research, teaching, and innovation and performs overarching services within KIT and for external parties.

We are looking for a researcher to work on uncertainty quantification and inverse problems in the setting of medical imaging problems. Specifically, the researcher will work on the development of efficient adjoint simulations for kinetic Monte Carlo simulations for radiative transport equations. Developing such simulations will require combining mathematical modeling techniques for deriving adjoint equations with programming techniques to produce an efficient implementation. In particular, a memory-efficient implementation is foreseen using reversible pseudorandom number generators. The ultimate goal is to integrate these techniques into an existing code, in collaboration with industrial partners.

This position offers the possibility to acquire a PhD degree while working in the CSMM research group, headed by Professor Martin Frank. This thesis will be carried out within the scope of an interuniversity BMTFR project under the funding theme “Mathematics for Innovation”, in collaboration with an industry partner. An industrial internship may also be possible within this framework. The successful applicant will also be associated with KIT’s KCDS graduate school.

• You have a master's degree in applied mathematics, computational science or a compatible STEM subject. You can demonstrate a solid foundation in programming through either academic projects or otherwise.
• Knowledge or experience in at least one of these methodological fields is an advantage: mathematical modeling, stochastic simulation and uncertainty quantification. Further, prior experience in C++ and/or CUDA is a plus.

Curious about an exciting and versatile role in an agile team? Discover more about SCC as your professional place to be: https://www.scc.kit.edu/en/aboutus/working-at-scc.php