We are looking for a motivated student to conduct their master thesis in the area of Li-ion batterie modelling using state-of-the-art machine learning modelling techniques. This master thesis focuses on developing advanced techniques to estimate the health estimation of battery health and performance in the automotive industry. By leveraging deep neural network architectures for learning the trajectory of the degradation with existing amount of test data, the aim is to estimate the state-of-health without having the entire history of the battery's operation (zero-shot learning). The thesis will contribute to the overcome practical issues for SOH estimation in-field and will offer valuable insights into understanding the influencing aging factors. Literature research: Identify the state-of-art for the specific applications and rank most relevant architectures/techniques Data preparation and pre-processing: Utilize time series analysis and aggregation techniques to create a pipeline for feature engineering during charge cycles. Selection of the target variables Data segmentation: Prepare sample of data from existing experimental datasets for training the models Comparison and ablation study: Establish a set of baseline methods (i.e., MLP, RNN, LSTM) that will be used for comparison purposes Final model evaluation: Utilize the trained models for final evaluation in both experimental and real-world data Sensitivity analysis: Utilize Explainable-AI methods to pinpoint influencing factors and explain model's outputs