PhD – Hybrid Modeling with Deep Generative Networks
- Robert-Bosch-Campus 1, 71272 Renningen, Germany
- Legal Entity: Robert Bosch GmbH
Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology – with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.
The Robert Bosch GmbH is looking forward to your application!
Hybrid modeling corresponds in machine learning to inductive bias design using environment physics as the key source of prior information. Exploiting those properties of a dynamic system that can be explained by well-known first principles, the learning model can preserve its capacity for the unexplained component, and this way can be more accurate while remaining maximally sample-efficient. This position aims to develop a PhD thesis on building an optimal learning framework that combines graph-based and physics-based modeling of inductive bias to model complex physical phenomena in an explainable manner. During your PhD you will be part of the active research team at the Bosch Center for Artificial Intelligence (BCAI, www.bosch-ai.com).
- Help shape the future: You develop novel hybrid predictors that effectively combine physical environmental knowledge and machine learning.
- Observe, and think ahead: You conduct prototypical algorithm implementations and benchmarking on real-world data sets.
- Take responsibility: You publish in top-tier conferences (ICML, NIPS, ICLR, AISTATS, etc.) and journals (JMLR, PAMI, etc.).
- Experience cooperation: You screen literature and build up close contact with the academic community.
- Networked communication: You participate in academic interactions within the BCAI research team.
- Personality: enthusiastic for science, excited to tackle theoretical challenges, team player
- Working Practice: perform exclusively academic research with excellence (i.e. no industry project duties), gradually develop scientific independence until graduation, make significant contributions to science
- Experience and Knowledge: general knowledge of machine learning, excellent theoretical skills proven by top course grades, prior experience on scientific writing is a plus, a background on Gaussian processes or Bayesian inference is preferable
- Enthusiasm: intrinsic motivation for top-level research and scientific independence
- Languages: fluent in English (written and spoken)
- Education: master degree in machine learning, mathematics, statistics, physics, computer science, or related fields with excellent grades
The final PhD topic is subject to your university.
Duration: 3 years
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223
Need further information about the job?
Melih Kandemir (Functional Department)
+49 711 811-52789