PhD - Uncertainty in Deep Learning
- 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!
- Create something new: A very important part of your PhD you develop and implement novel deep learning algorithms and research on uncertainty estimation, with the focus on generative modelling and Bayesian inference.
- Structured evaluation: Additionally, you evaluate developed algorithms on open datasets and on multi-modal Bosch datasets from application areas such as autonomous driving, robotics, and Internet of Things.
- Experience cooperation: You collaborate with machine learning and computer vision experts and publish in top-tier journals and conferences.
- Conscientious coordination: Last but not least, you will participate in technical discussions and create new ideas within the deep learning research team at the BCAI.
- Education: Excellent degree (Master/Diploma) in computer science, mathematics, or related fields with excellent marks.
- Personality: Strong team player, highly self-motivated and motivated to work in an interdisciplinary and international team
- Working Practice: Analytical thinking, very good academic writing and excellent communication skills
- Experience and Knowledge: Experience with deep learning frameworks (TensorFlow, PyTorch, etc.), basic knowledge of machine learning and deep learning and prior experience with generative modelling and/or Bayesian inference is a plus
- Qualifications: Strong programming skills, in particular Python
- Languages: Very good in English (written and spoken)
Please submit all relevant documents (incl. curriculum vitae, certificates).
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?
Michael Pfeiffer (Functional Department)
+49 711 811 18195