Causal Discovery Expert for Vehicle Quality Data
vor 3 Wochen
Unlock the potential of causal discovery for vehicle quality data with our cutting-edge research project. As a master thesis candidate, you will work with state-of-the-art Transformer models to understand the relationships between variables and develop predictive maintenance strategies.
Key Responsibilities:
- Apply Transformer models to causal discovery for vehicle quality data
- Work with real-world datasets to draw applicable conclusions
- Push the boundaries of predictive maintenance using deep learning approaches
- Gain practical AI experience by implementing PyTorch and Hugging face models
- Present the thesis orally and through a research paper
Requirements:
- Master student in computer science or related fields with focus on machine learning or artificial intelligence
- Strong knowledge in deep learning, transformers, graphs, and NLP
- Excellent programming skills in Python, PyTorch, and experience with modern cloud environment tools such as AWS
- Fluent English speaker
- Driven by curiosity and motivated to solve scientific problems independently as well as sharing ideas and working in a team
What We Offer:
- Comprehensive mentoring and onboarding
- Personal and professional development
- Flexible working hours
- Digital offers and mobile working
- Attractive remuneration
- Apartment offers for students (subject to availability and only Munich)
We are an equal opportunities employer and welcome applications from diverse candidates. Apply now to join our team and contribute to cutting-edge research in causal discovery for vehicle quality data.
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