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Job Title: Thesis - Self-supervised Learning for Battery Health Estimation (f/m/d), Graz
Client:
AVL List GmbH
Location:
Graz, Austria
Job Category:
Other
EU Work Permit Required:
Yes
Job Reference:
dac0d1b5455a
Job Views:
5
Posted:
27.04.2025
Expiry Date:
11.06.2025
Job Description:
We are seeking a motivated student to conduct their master thesis on Li-ion battery modeling using advanced machine learning techniques. The focus is on developing methods to estimate battery health and performance in the automotive industry, leveraging deep neural networks to learn degradation trajectories with existing test data. The goal is to estimate the state-of-health without requiring the entire operational history of the battery (zero-shot learning). This research aims to address practical challenges in in-field SOH estimation and provide insights into aging factors.
Responsibilities:
1. Literature research: Identify and rank the most relevant architectures and techniques in the current state-of-the-art.
2. Data preparation and pre-processing: Use time series analysis to create feature engineering pipelines during charge cycles, selecting target variables.
3. Data segmentation: Prepare experimental datasets for training models.
4. Comparison and ablation study: Establish baseline models (e.g., MLP, RNN, LSTM) for comparison.
5. Model evaluation: Assess trained models with experimental and real-world data.
6. Sensitivity analysis: Use Explainable AI methods to identify influential factors and interpret model outputs.
Minimum Qualifications:
* BSc in Applied Statistics, Mathematics, Computer Science, Data Science, Automotive, or Electrical Engineering or related fields.
* Strong background in data analysis, deep learning, and time series prediction.
* Proficiency in Python programming.
* Familiarity with statistical methods and transformer-based models (LLMs).
* Ability to work independently, conduct experiments, and analyze complex datasets.
* Excellent problem-solving and critical-thinking skills.
* Strong communication skills for presenting findings effectively.
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