PhD - Embedding Hardware Primitives into 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!
Deep neural networks are the method of choice for many real-world applications, but their resource-efficient execution in both embedded devices and data centers remains a challenge. The goal of this PhD position at the Bosch Center for Artificial Intelligence (BCAI) is to develop innovative approaches to improve the power-efficiency of deep neural networks including:
- Create something new: Be part of original research on new computational primitives and compression methods towards energy-efficient deep neural networks, in addition, collaborations, technical discussions and creation of new ideas with deep learning and hardware experts at Bosch Corporate Research.
- Integrated implementation: You develop and implement network architectures and training algorithms.
- Take responsibility: Moreover, you supervise master students.
- Networked communication: You publish in top-tier journals.
- Education: Excellent degree (Master of Science) in computer science, mathematics, physics, engineering or related fields with excellent grades
- Personality: Strong team player and ability to work in interdisciplinary teams
- Experience and Knowledge: Profound knowledge of Machine Learning, preferably Deep Learning, basic knowledge about computer software and hardware,
- Qualifications: Proven programming skills, preferably Python and/or C++
- Languages: Fluent in English written and spoken
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223
Need further information about the job?
Thomas Pfeil (Functional Department)
+49 711 811 48224