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researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will be to develop efficient methods for Hybrid Learning-Control Methods for Autonomous
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16th October 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate in Hybrid Learning-Control Methods for Autonomous Underwater
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distances from the spatial structure of genetic genealogies across the genome, and the genetic relatedness among individuals; or develop and use biophysical models (hydrodynamics + particle tracking
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research direction is the integration of strategic transport models, such as discrete choice modelling (DCM) with agent-based modelling (ABM), to examine potential transport behaviour under different
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administrative workload, the standardization of complex assessments, and reduced professional discretion. There is therefore a need for more research examining how digitalization unfolds in practice. The PhD
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Particle Fluid Dynamic (CPFD) model development. The person appointed will be affiliated with the Research Group in Energy and Environmental Technology (URGENT). Duties Complete the doctoral programme up
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absorption in gasification of biomass. The overall approach is lab-scale experiments in combination with Computational Particle Fluid Dynamic (CPFD) model development. The person appointed will be affiliated
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regulatory learning and foresight at TUM, methods and analytics at BI, and academic writing and impact at the University of St. Gallen. You will also take part in methods workshops, monthly paper development
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particles) are partly available from previous campaigns but will be supplemented with new data collected during the PhD project. Vertical carbon export will be investigated with long-term sediment traps
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summer schools, covering regulatory learning and foresight at TUM, methods and analytics at BI, and academic writing and impact at the University of St. Gallen. You will also take part in methods workshops