PhD – Hybrid (AI-driven and physics based) Models for Dynamical Systems
- Robert-Bosch-Campus 1, 71272 Renningen, Germany
- Legal Entity: Robert Bosch GmbH
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Hybrid modeling of a dynamical system leverages prior domain knowledge in guiding data-driven machine learning towards more informed, explainable predictive models. While model-based simulations aim at modeling stable causal and physical relationships and machine learning excels in mining hidden patterns in measurements, hybrid modeling tries to harness the best of both approaches.
In this PhD project, the objective is to develop an automated framework for hybrid model generation, training and evaluation. An important decision in this framework is the selection of the most appropriate hybridization paradigm (accompanied with its proper training method) for a given task. For this purpose, careful design of well-defined selection criteria is required for targeting an intuitive guideline for model selection. Additionally, superior performance of selected models need to be validated not only on attainable real-world measurements but also as to which extend physical constraints have been satisfied. During your PhD, you will be part of the active research team at the Bosch Center for Artificial Intelligence (BCAI).
- Right from day one, you will invent novel approaches for model hybridization.
- You will conduct prototypical implementations and benchmarking on both synthetic scenarios and real-world data sets.
- Furthermore, you publish in top-tier conferences (ICML, NIPS, ICLR, AISTATS etc.) and journals (JMLR, PAMI etc.).
- You screen literature and build up close contact with the academic community.
- Be part of the team and participate in academic interactions within the BCAI research team and have the chance of integrating your developments in real industrial applications.
- Education: you have achieved an excellent Master of Science degree in mathematics, computer science, cybernetics, physics or comparable subject
- Personality and Working Practice: Strong and motivated team player who has the ability to independently pursue research work and likes to work in an interdisciplinary and international team
- Experience and Knowledge: Experience in development and implementation of state-of-the-art machine learning technologies and proven programming skills, in particular Python or Matlab
- Languages: fluent in English (written and spoken)
The final PhD topic is subject to your university. Duration: 3 years
Please submit all relevant documents (incl. curriculum vitae, transcripts, certificates, motivation letter, publications, blog posts and GitHub repos, if available)
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Kevin Heiner (Human Resources)
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Need further information about the job?
Karim Barsim (Functional Department)
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