Description
You will develop graph and time-series machine learning models that diagnose industrial process faults and estimate their location and severity, working closely with academic research groups.
Tasks
- Develop graph and time-series models and benchmark them on industrial datasets
- Improve how models explain faults, estimate severity and report uncertainty
- Build models capturing plant dynamics, scaling from single processes to whole plants
- Bring validated research into the product to help engineers detect faults early
Profile
- Ideally a PhD in a related field
- Strong background in graph machine learning and time-series learning for physical or industrial systems
- Alternatively, strong background in dynamical systems, system identification and control
- Experience in both areas is ideal, depth in either is welcome
- Interest in research and in making models work in industrial operations
Offer
- Full-time position
- Start by April 2027
- 3–6 months interim, then a permanent position
Company
Industrial AI research