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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. </strong><strong>Join in and feel the difference.</strong><br> <br> The<em> </em>Robert Bosch GmbH<em> </em>is looking forward to your application!</p></div></section><section><div><p></p><h2>Job Description</h2></div><div><ul><li>During your internship you will conduct a literature review of classical electrochemical battery models, physics-informed neural networks, and hybrid modeling techniques applied to high-voltage EV traction batteries.</li><li>You will investigate and compare methods of incorporating physical laws into neural networks, evaluating their strengths and weaknesses to provide a comprehensive overview.</li><li>Furthermore, you will develop a hybrid model that integrates electrochemical physics-based models with data-driven methods to address limitations of common electrochemical models.</li><li>You will implement the most promising physics-informed neural network approaches using Python and suitable deep learning frameworks. You will also train and calibrate the implemented models using laboratory and real-world battery telemetry data, performing hyper-parameter tuning and rigorous validation, including sensitivity analysis, to assess reliability and generalizability.</li><li>Additionally, you will analyze and optimize model performance focusing on accuracy, robustness, and computational efficiency.</li><li>Finally, you will compile research findings into a clear and concise internship report or master thesis, detailing the hybrid modeling approach, implemented models, and performance results.</li></ul></div></section><section><div><p></p><h2>Qualifications</h2></div><div><ul><li><strong>Education: </strong>Master studies in the field of Physics, (Electro-)Chemistry, Applied Mathematics, Computer Science, Electrical Engineering or comparable with good grades</li><li><strong>Experience and Knowledge: </strong>proficient in Python and MATLAB; familiar with deep learning frameworks (e.g. TensorFlow, PyTorch); knowledge in electrochemical battery models, machine learning, data analysis and computational modeling</li><li><strong>Personality and Working Practice: </strong>independent and analytical working style</li><li><strong>Enthusiasm: </strong>for Electromobility, Lithium-Ion Battery Technology and Programming</li><li><strong>Languages: </strong>very good in English</li></ul></div></section><section><div><p></p><h2>Additional Information</h2></div><div><p><strong>Start:</strong> according to prior agreement<br> <strong>Duration:</strong> 6 months (confirmation of mandatory internship required)</p><p>We offer you</p><ul><li>35 hours/week with flextime</li><li>a permanent contact person who will accompany you during your internship</li><li>a modern working environment, as well as mobile working by arrangement</li><li>the opportunity to become part of our student network students@bosch Stuttgart</li><li>discounts in our company restaurants</li></ul><p>Requirement for this internship is the enrollment at university. Please attach your CV, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit.</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 further information about the job?</strong><br> Christoph Kröner (Functional Department)<br> +49 152 04219386</p><p>#LI-DNI<strong> </strong></p></div></section></div>

Mandatory Internship in Hybrid Modeling of High-Voltage Traction Batteries Using Physics-Informed Neural Networks