system-administration

Bosch Group

<div><section><div><p></p><h2>Company Description</h2></div><div><p><strong>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.</strong></p><p>The Robert Bosch GmbH is looking forward to your application!</p></div></section><section><div><p></p><h2>Job Description</h2></div><div><p>Current automated driving systems are structured in a hybrid manner, meaning that classical model-driven approaches are combined with data-driven AI approaches. So far all modules are developed or trained independently based on module-related key performance indicators. As a second step system-related evaluation is conducted in software-in-the-loop tests and closed-loop in the vehicle. The main drawbacks are on the one hand, that the independent optimization of modules offers no guarantee of a global system-related optimum and on the other hand, the manual experience-based analysis of the resulting system to determine the performance-related weak points in the architecture. The slow system design process is also a disadvantage.</p><ul><li>The goal of this PhD thesis is to research the system aspects of a ground breaking innovation in system design: End-2-End trainable systems.</li><li>You will edit following research questions: How to efficiently train a system that is composed of AI sub-modules (efficient loss propagation)? How to train a hybrid system? And you will also research safety-related measures that allow introspection of the system.</li><li>In your PhD thesis you will develop novel machine learning approaches with a focus on Video, Radar and LiDAR.</li><li>Furthermore, you will evaluate your algorithms on public benchmark data sets and internal real-world data sets - offline as well as online.</li><li>In addition, you will contribute to the scientific community with publications on top machine learning, system and robotics conferences as well as journals (NIPS, ICML, ICLR, CVPR, ICCV, IROS, TPAMI or IV).</li><li>Last but not least, you will take responsibility and work in an agile as well as diverse research team with other PhD students and with a strong link to autonomous driving system research projects.</li></ul></div></section><section><div><p></p><h2>Qualifications</h2></div><div><ul><li><strong>Education: </strong>excellent degree (Master/Diploma) in Computer Science, Robotics, Electrical Engineering, Mathematics or related field</li><li><strong>Experience and Knowledge: </strong>profound knowledge of machine learning algorithms and principles, preferably deep learning as well as proven programming skills in Python and C++</li><li><strong>Personality and Working Practice: </strong>open-minded, logical thinking, goal- als well as team-oriented</li><li><strong>Languages: </strong>fluent in English (written and spoken), German is a plus</li></ul></div></section><section><div><p></p><h2>Additional Information</h2></div><div><p><a href="https://www.bosch-ai.com">https://www.bosch-ai.com<br> www.bosch.com/research</a></p><p>The final Phd topic is subject to your university.</p><p>Start: according to prior agreement</p><p>Please submit all relevant documents (incl. curriculum vitae and certificates).</p><p>You want to work remotely or part-time - we offer great opportunities for mobile working as well as different part-time models or job-sharing. Feel free to contact us.</p><p>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.</p><p><strong>Need support during your application?</strong><br> Kevin Heiner (Human Resources)<br> +49 711 811 12223</p><p><strong>Need further information about the job?</strong><br> Thomas Michalke (Functional Department)<br> +49 711 811 43435</p></div></section></div>

PhD - End-2-End Trainable System for Autonomous Driving with Introspection

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