Master Thesis Reliability Analysis and Uncertainty Quantification Using Generative AI for Batte[...]

TN Germany; Stuttgart; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

An innovative opportunity awaits with a forward-thinking company looking for a Master's thesis candidate in Reliability Analysis and Uncertainty Quantification using Generative AI. This role involves exploring cutting-edge technologies to enhance battery diagnostics within automotive onboard power supply systems. You will have the chance to develop probabilistic models, assess uncertainties, and contribute to significant advancements in automotive technology. Join a collaborative environment where your insights and creativity can drive impactful solutions in the automotive industry.

Qualifikationen

• Master studies in any field required.
• Background in Machine Learning and Deep Learning preferred.

Aufgaben

• Conduct extensive review of methodologies for uncertainty quantification.
• Implement and compare different UQ models.

Kenntnisse

Machine Learning

Deep Learning

Reliability Analysis

Uncertainty Quantification

PyTorch

Battery Diagnostics

Ausbildung

Master's Degree

Tools

Conditional Generative Adversarial Networks (cGAN)

Conditional Normalizing Flows (cNF)

Jobbeschreibung

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Master Thesis Reliability Analysis and Uncertainty Quantification Using Generative AI for Battery Diagnosis within Automotive Onboard Power Supply Systems, Stuttgart

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Client:

Location:

Stuttgart, Germany

Job Category:

Other

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EU work permit required:

Yes

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Job Reference:

a8797e6b35ae

Job Views:

2

Posted:

27.04.2025

Expiry Date:

11.06.2025

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Job Description:

Master Thesis Reliability Analysis and Uncertainty Quantification Using Generative AI for Battery Diagnosis within Automotive Onboard Power Supply Systems

Full-time

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. Join in and feel the difference.

The Robert Bosch GmbHis looking forward to your application!

Job Description

With the emerging technologies like autonomous driving and x-by-wire systems, the vehicle's onboard power supply system, also known as the powernet, is subject to stringent safety requirements. Failure of the powernet leads immediately to the loss of all the safety-related functions such as braking, steering, autonomous driving features, etc. Among all the powernet components, special attention shall be paid on batteries due to their complex electrochemical nature. Accurate battery diagnosis is essential to predict the performance of batteries. Safety validation is, therefore, required to guarantee that the prediction deviations is acceptably low under all real-world conditions. To ensure the robustness of battery diagnosis under real-world conditions, uncertainty quantification (UQ) is necessary to assess the prediction deviations. This thesis aims to develop a probabilistic surrogate model using generative AI models (e.g., Conditional Generative Adversarial Networks (cGAN), Conditional Normalizing Flows (cNF) to quantify and distinguish different sources of uncertainty. Additionally, the study will explore strategies to estimate failure probabilities with a limited amount of data while maintaining high accuracy. The research questions are: how to quantify and separate epistemic and aleatory uncertainties, which generative AI models are best suited for capturing aleatory uncertainties, how to evaluate and calibrate the reliability of the predicted uncertainty, how does the amount of available data impact the accuracy of uncertainty quantification, how to estimate failure probability effectively with a small data set.

• As part of your Master thesis, you will conduct an extensive review of the state-of-the-art methodologies for uncertainty quantification (UQ) based on generative AI.
• You will prepare synthetic data for study.
• Furthermore, you will implement and compare different UQ models in terms of their ability to accurately capture predictive uncertainty and to distinguish between epistemic and aleatory uncertainty.
• In addition, you will improve the prediction interval calibration to ensure a robust and reliable uncertainty estimate.
• Last but not least, you will develop an approach to estimate the probability of battery diagnostic failure with minimal data using probabilistic surrogate models.

Qualifications

• Education: Master studiesin any field
• Experience and Knowledge: background in Machine Learning, Deep Learning, Reliability or Uncertainty quantification, etc.; experience with PyTorch and deep generative models (cGANs, cNF, etc.); knowledge of battery diagnostics or battery systems is a plus
• Personality and Working Practice: you are a self-motivated and proactive person who is able to work independently
• Languages: very good communication skills in written and spoken German or English

Additional Information

Start: according to prior agreement
Duration: 3 - 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

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 further information about the job?
Zhiyi Xu (Functional Department)
[phone]