ML Engineer for Li-Ion Battery Voltage Prediction and System Identification
vor 16 Stunden
AVL List GmbH is a leading technology company that develops pioneering solutions for the international automotive industry, transportation, and machinery industries.
SalaryThe remuneration for this position includes a one-time fee paid as compensation for the successful completion of the thesis. The estimated annual salary is approximately 24,000 - 30,000 euros, depending on individual qualifications and experience.
Job DescriptionWe are seeking a highly motivated Master's or Bachelor's student in Electrical Engineering, Computer Science/Data Science or Digital Engineering to work on a research project focused on machine learning methods for Li-Ion battery voltage prediction and system identification. The successful candidate will have the opportunity to develop and implement different ML algorithms, such as neural networks, long short-term memory (LSTM) models, decision trees, and Gaussian processes, to predict the output voltage of a Li-Ion battery. The project also involves comparing at least two different ML algorithms with R&C parameter estimation.
Required Skills and Qualifications- Good knowledge of English
- Programming skills in Python
- Knowledge of optimization methods and machine learning
As a valued member of our team, you can expect:
- To write your thesis independently and receive professional guidance and support from our experienced employees.
- To have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
- To immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.
At AVL, we foster and celebrate diversity: We recognize that diverse ways of thinking are required to achieve our vision of a greener, safer, and better world of mobility. Different backgrounds, attitudes, interests, and experiences make us successful. As an Equal Opportunity Employer, we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation, or disability status.
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