PhD Candidate: Temporal-Spatiotemporal ML for Transport

Massimiliano Ruocco; Trondheim; On-site; NOK 522,000 - 638,000; Full time

Benefits offered by this job

High-performance computing resources

Mentor programme

Pension benefits

Free Norwegian language training

Job summary

NTNU's Department of Computer Science in Trondheim invites applications for a four-year PhD candidate position in machine learning for temporal and spatiotemporal data. The project focuses on forecasting and disruption localization on graphs, with STRIDE as the main testbed in transport networks.

You will develop deep learning methods, publish at leading conferences, and work under Associate Professor Massimiliano Ruocco within the DART group.

Qualifications

• Master's degree with a major independent project (min 30 ECTS) equivalent to a master’s thesis.
• Strong background in deep learning and probability/statistics.
• Good programming skills in Python and experience with PyTorch or JAX.
• English oral and written presentation skills.

Responsibilities

• Complete the doctoral education until obtaining a doctorate.
• Carry out high-quality research within the described framework.
• Develop, implement and evaluate deep learning methods and release code and benchmarks openly.
• Publish academically and disseminate results to a broader audience.
• Participate in the Data and Artificial Intelligence (DART) group and STRIDE activities.
• Take part in international conferences and research stays; undertake teaching duties.

Skills

Deep learning

Python

PyTorch or JAX

English communication

Education

Master's degree in CS/ML or equivalent

Tools

PyTorch

JAX

Python

Job description

NTNU's Department of Computer Science in Trondheim invites applications for a four-year PhD candidate position in machine learning for temporal and spatiotemporal data. The project focuses on forecasting and disruption localization on graphs, with STRIDE as the main testbed in transport networks.

You will develop deep learning methods, publish at leading conferences, and work under Associate Professor Massimiliano Ruocco within the DART group.