Mohammed VI Polytechnic University
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Postdoctoral Researcher in Artificial Intelligence and Large Language Models for Predictive Maintenance Mohammed VI Polytechnic University in Morocco
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Dec 4, 2026
Country
Morocco
University
Mohammed VI Polytechnic University
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About this position
Mohammed VI Polytechnic University is seeking a postdoctoral researcher in Artificial Intelligence, with a focus on applying Large Language Models (LLMs) to predictive maintenance. The work is based at the university’s research environment in Benguerir, Morocco, within an institution focused on research, innovation, and technology-driven development in Africa.
The researcher will investigate how LLMs can support predictive maintenance by analyzing and interpreting unstructured industrial information, including maintenance logs, sensor data, and technical reports. The project will explore the development and fine-tuning of LLMs for predictive insights, with applications in anomaly detection, failure prediction, and maintenance-schedule optimization.
The role involves collaboration with domain experts to integrate LLM-based methods into predictive-maintenance workflows. The researcher will also contribute to scalable and interpretable AI tools intended for real-world industrial deployment, while advancing research at the intersection of artificial intelligence, natural language processing, and industrial applications.
The research environment offers opportunities to develop work using modern deep-learning and transformer-based methods and to publish findings in leading AI, machine learning, and NLP conferences and journals.
Funding details
Available
What's required
Applicants should have recently completed or be close to completing a PhD in Computer Science, Machine Learning, Natural Language Processing, or a related field, with a thesis focused on AI, particularly Large Language Models. A strong publication record in leading AI, machine learning, or NLP conferences is expected. Applicants should be proficient in Python and experienced with deep-learning frameworks such as TensorFlow, PyTorch, or JAX, with an in-depth understanding of transformer architectures, attention mechanisms, and LLM fine-tuning. Experience with time-series data, anomaly detection, or predictive maintenance is strongly preferred; familiarity with industrial datasets and domain-specific challenges is desirable. Excellent problem-solving skills and the ability to work independently and collaboratively are also required.
How to apply
Submit a cover letter explaining your research interests and their alignment with LLMs for predictive maintenance, a current CV with publication list, contact information for at least three references, and a research statement of no more than two pages describing past and future research. Apply through the listed EURAXESS or Times Higher Education job page.
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Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.