PhD - Property Prediction for Embedded (AI) Systems

Bosch Group; Deutschland; Vor Ort; EUR 20.000 - 40.000; Vollzeit

Zusammenfassung

A leading technology company in Germany is seeking a PhD candidate to explore the modeling and performance estimation of deep learning systems. The role involves investigating hardware characteristics for efficient machine learning applications. Candidates should hold a Master's degree in relevant engineering disciplines and possess strong programming skills. This position offers an opportunity to work in a collaborative research environment with a focus on innovation.

Qualifikationen

• Proficiency in programming languages (C/C++, Python, Matlab).
• Experience with Machine Learning techniques like TensorFlow and PyTorch.
• Knowledge of digital hardware and SoC architectures.
• Fluent in English, German is an advantage.

Aufgaben

• Investigate hardware characteristics for modeling and performance estimation.
• Explore machine learning methods for performance prediction.
• Discover machine learning compilers for modeling workflow optimizations.

Kenntnisse

Proficiency in programming languages (C/C++, Python, Matlab)

Good skills in modern mathematics, e.g. Machine Learning

Knowledge of AI algorithms

Experience in digital hardware and embedded systems

Fluent in English

Ausbildung

Excellent degree (Master or Diploma) in Electrical Engineering, Information Engineering, Microelectronics or Informatics

Jobbeschreibung

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our promise to our associates is rock-solid: We grow together, we enjoy our work, and we inspire each other. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Job Description

Recent advances in deep learning (DL) provide high accuracy for various tasks targeting a wide range of applications ranging from Tiny ML that typically run on low power devices up to foundation and large language models running on cloud-based systems. One core discipline in the development process lies the modeling and performance estimation of the target system. With this work we want to push the boundaries of the current state of the art to adapt to the increasingly rapid development cycles with new approaches to faster predict and evaluate the performance behavior of future systems.

• In this PhD project, you will investigate how to extract different hardware characteristics and the possible ways to model them at different levels of abstractions, efficiency, as well as accuracy.
• You will inspect how to use these characteristics to predict performance for different workloads on a target hardware platform and across platforms.
• As a part of our team, you will explore different novel machine learning based methods including their applicability and efficiency compared to the conventional modeling methods.
• Furthermore, you will discover different machine learning compilers and their usage as part of the modeling workflow and the related optimizations that can facilitate more efficient as well as accurate predictions.

Qualifications

• Education: excellent degree (Master or Diploma) in Electrical Engineering, Information Engineering, Microelectronics or Informatics
• Experience and Knowledge: proficiency in programming languages (C/C++, Python, Matlab), good skills in modern mathematics, e.g. Machine Learning (TensorFlow, PyTorch, etc.), Solver, Neural Networks, knowledge of AI algorithms, experience in digital hardware, embedded systems as well as in SoC architectures
• Personality and Working Practice: you enjoy being creative and asserting yourself in certain topics; you like working in a team and understand how to think in a structured, abstract, and strategic way to achieve the best possible performance
• Languages: fluent in English, German is an advantage

Additional Information

https://www.bosch-ai.com
www.bosch.com/research

Start: March 2025

Please submit all relevant documents (CV, letter of motivation, certificates, and links to GitHub or kaggle account).

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need support during your application?
Sarah Schneck (Human Resources)
[phone]

Need further information about the job?
Falk Rehm (Functional Department)
[phone]

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