"Postdoctoral Researcher in Artificial Intelligence for Predictive Maintenance"

Context

We are seeking a highly motivated and skilled postdoctoral researcher with a strong background in Artificial Intelligence (AI), particularly in the development and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply their expertise to advance predictive maintenance systems using AI tools.

Key Responsibilities:

• Conduct innovative research on the application of Large Language Models (LLMs) to predictive maintenance challenges.
• Develop and fine-tune LLMs to analyze and interpret unstructured data (e.g., maintenance logs, sensor data, technical reports) for predictive insights
• Collaborate with domain experts to integrate LLM-based solutions into predictive maintenance workflows.
• Explore the use of LLMs for anomaly detection, failure prediction, and optimization of maintenance schedules.
• Publish high-impact research in top-tier conferences and journals at the intersection of AI, NLP, and industrial applications.
• Contribute to the development of scalable and interpretable AI tools for real-world deployment.

Qualifications:

• A PhD in Computer Science, Machine Learning, NLP, or a related field, with a thesis focused on AI, particularly LLMs.
• Strong publication record in top AI/ML/NLP conferences (e.g., NeurIPS, ICML, ACL, EMNLP, etc.).
• Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX.
• In-depth understanding of transformer architectures, attention mechanisms, and fine tuning techniques for LLMs.
• Experience with time-series data, anomaly detection, or predictive maintenance is a strong plus.
• Familiarity with industrial datasets and domain-specific challenges is desirable.
• Excellent problem-solving skills and the ability to work both independently and collaboratively in a team environment.

How to Apply:

Interested candidates should submit the following documents:

1. A cover letter detailing your research interests and how they align with the application of LLMs to predictive maintenance.
2. A current CV, including a list of publications.
3. Contact information for at least three references.
4. A brief research statement (max 2 pages) outlining your past research and future research directions, particularly as they relate to LLMs