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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>Together with our L4 automated driving team, we work on novel concepts to solve perception tasks such as detection and tracking of traffic participants. In particular, we combine temporal information (i.e., reasoning considering the history) and information from multiple sensors (i.e., the combination of several cameras into a common world model).</p><ul><li>In this PhD thesis, you will explore more advanced ideas related to this task with the goal of reducing the need for manually labeled data while maintaining the same model performance and robustness.</li><li>In a first setting, you aim to improve monocular depth estimation using video data from multiple cameras. Here, you will further exploit the temporal and spatial relationship of features observed in different sensors and acquisition times and evaluate their impact on model performance. We believe that this enables a continual, label efficient learning of precise depth estimators, used as prerequisite for other downstream tasks.</li><li>Furthermore, you will aim to improve models for static and dynamic scene reconstruction (in 3D or bird's eye view) based on multiple view sequence data in order to provide a compact and meaningful representation for subsequent automated driving tasks such as trajectory planning and behavior generation.</li><li>As a PhD student in our AI research team you will be guided by an experienced team member who will help you catching up with state-of-the-art methods in the beginning and serve as a discussion partner during your time in our team. You will be provided a modern working environment, flexible working hours and gain access to our high-perfomance computing infrastructure for conducting experiments.</li></ul></div></section><section><div><p></p><h2>Qualifications</h2></div><div><ul><li><strong>Education:</strong> excellent Master's degree in Computer Science, Mathematics, Engineering or similar</li><li><strong>Personality and </strong><strong>Working Practice: </strong>systematic, creative and self-dependent such as highly motivated to solve complex real-world problems</li><li><strong>Experience and Knowledge: </strong>general knowledge on machine learning/deep learning methods, good software engineering skills, in particular Python and a ML framework (e.g. torch, tensorflow, jax)</li></ul></div></section><section><div><p></p><h2>Additional Information</h2></div><div><p><a href="http://www.bosch.com/research">Bosch Research</a><br><a href="https://www.bosch-ai.com">Bosch Center for Artificial Intelligence (BCAI)</a></p><p>Please submit all relevant documents (incl. motivation letter, curriculum vitae, and transcript of records).</p><p>Feel free to reach out to us at any time in case you have questions. – See you in our team!</p><p>Apply now in just 3 minutes!</p><p>You want to work remotely - we offer great opportunities for mobile working. Feel free to contact us.</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>Sascha Wirges (Functional Department)<br>+49 172 7361654</p></div></section></div>

PhD – Label Efficient Machine Learning on Multi-View Image Sequences

remote

14 days ago