These collaborations are named because shared research is already public. Each record connects the relationship to publications, software, data, supervision, or another public scientific contribution, demonstrating technical capability and joint research delivery.
01
Industrial microfluidics · funded doctoral research
Bosch Corporate Research · TU Darmstadt
From wetting experiments to predictive microfluidic simulation
Four fully funded doctoral projects connect Bosch experiments and application knowledge with numerical methods, automated image analysis, and resolved multiphase CFD at TU Darmstadt.
My role: Principal Investigator and primary academic supervisor; leadership in numerical methods, simulation, software, and the connected publication program.
Public record
Four peer-reviewed studies now form a connected evidence chain for stable capillary-flow simulation, dynamic wetting, interface pinning, and three-dimensional microcavity filling.
Hewlett Packard Enterprise · TU Darmstadt · TU Dresden
Online CFD-machine-learning coupling with OpenFOAM and SmartSim
Joint work with HPE researchers created a scalable, in-memory connection between parallel OpenFOAM simulations and machine-learning workflows using SmartSim and SmartRedis.
My role: Scientific architect and co-developer of the OpenFOAM-SmartSim integration, publication, executable examples, and training workflows.
Public record
The collaboration produced a peer-reviewed open-access paper, a reusable open-source integration, executable examples, and public training formats.
Rimac Automobili · University of Manchester · TU Darmstadt
Bayesian design optimization for microfluidic chip cooling
An industry-funded doctoral project connects Rimac's thermal-management application with joint academic supervision at the University of Manchester and TU Darmstadt.
My role: Primary TU Darmstadt supervisor and co-developer of the CFD and Bayesian-optimization methods, with joint academic supervision at the University of Manchester.
Public record
The public research program develops CAD-native Bayesian optimization for expensive three-dimensional CFD problems with manufacturability constraints, including mixed discrete-continuous design spaces and multiple objectives.
I work with research and engineering teams on verified simulation, experiments, data, optimization, agentic computational workflows, and durable software—with V&V and human review governing accepted results.