PhD – Data Efficient 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: You develop and implement novel Deep Learning algorithms. Furthermore, you evaluate these developed algorithms on open datasets and on multi-modal Bosch datasets from application areas such as autonomous driving, robotics, and Internet of Things.
- Help shape the future: You carry out research on generative models such as Generative Adversarial Networks, with the focus on relaxing the requirement of deep learning for large amounts of labeled data.
- Observe, and think ahead: You take part in technical discussions and creation of new ideas within the Deep Learning research team at the Bosch Center for Artificial Intelligence.
- Experience cooperation: You collaborate with Machine Learning and Computer Vision experts.
- Networked communication: You publish in top-tier journals and conferences.
- Personality: highly self-motivated and strong team player
- Working Practice: excellent communication skills and analytical thinking
- Experience and Knowledge: basic knowledge of Machine Learning and Deep Learning, experience with Deep Learning frameworks (TensorFlow, PyTorch, etc.), prior experience with generative modelling is a plus
- Qualifications: strong programming skills, in particular Python
- Enthusiasm: motivation to work in an interdisciplinary and international team
- Languages: very good English skills and academic writing skills
- Education: Master of Science in Computer Science, Mathematics, 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?
Michael Pfeiffer (Functional Department)
+49 711 811-18195