Research Scientist in Probabilistic Modeling

  • Robert-Bosch-Campus 1, Renningen, Germany
  • Full-time
  • Legal Entity: Robert Bosch GmbH

Company Description

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!

Job Description

  • Development and implementation of novel machine learning algorithms with focus on industrial application domains
  • Algorithmic focus on probabilistic machine learning approaches, e.g. probabilistic graphical models, (sparse) kernel methods, approximate inference techniques, latent variable models, etc.
  • Evaluation and comparison of machine learning algorithms for real-world applications, e.g. future mobility solutions, electric driving, connected manufacturing, etc.
  • Technical discussions and creation of new ideas & applications within the machine learning research team at Bosch Center for AI (BCAI, https://www.bosch-ai.com/)
  • Close contact to the scientific machine learning community, scouting and assessment of new approaches
  • Publications on top-tier conferences and journals

Qualifications

  • PhD in Machine Learning with excellent publication record in leading machine learning conferences or journals
  • Experience in development and implementation of state-of-the-art machine learning technologies
  • Broad knowledge of machine learning algorithms and principles
  • Proven programming skills, in particular in Python or Matlab
  • Strong teamplayer
  • Motivation for challenging tasks
  • Motivation to work in an interdisciplinary and international team
  • Strong English skills

Additional Information

Need support during your application?
Michael Streitmayer (HR Department)
+49 711 811 33306

Need further information about the job?
Heiner Markert (Business Department)
+49 711 811 42260

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