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.
- 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)
- Education: excellent degree (Master, Diplom) in computer science, mathematics, or related fields with excellent marks.
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