PhD Candidate: Deep Learning for Temporal & Spatiotemporal Data

NTNU - Norwegian University of Science and Technology; Trondheim; On-site; NOK 522,000 - 638,000; Full time

Benefits offered by this job

Free Norwegian language training

Access to high-performance computing

International research environment

Job summary

NTNU is offering a four-year PhD position in machine learning for temporal and spatiotemporal data in Trondheim. The project is part of STRIDE, focusing on forecasting and disruption localization on transport networks.

The candidate will develop deep learning methods, publish results, and collaborate across NTNU faculties. The main supervisor is Associate Professor Massimiliano Ruocco; English proficiency and strong ML background are required.

Qualifications

• Master's degree in computer science, machine learning, statistics, applied mathematics, physics or equivalent (minimum 30 ECTS in major independent project).
• Strong background in deep learning, probability, statistics and linear algebra.
• Excellent programming skills in Python; experience with PyTorch or JAX.
• Experience with transformers and foundation models, graph neural networks, time-series forecasting, anomaly detection.
• Good oral and written English.

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; release code and benchmarks openly.
• Publish academic work and disseminate science to broad audiences.
• Collaborate within STRIDE and with other PhD candidates and international partners.
• Participate in international conferences / research stays.
• Teaching and other career-promoting activities (one year total).

Education

Master's degree in computer science or related field

Tools

GitHub / GitLab

Job description

NTNU is offering a four-year PhD position in machine learning for temporal and spatiotemporal data in Trondheim. The project is part of STRIDE, focusing on forecasting and disruption localization on transport networks.

The candidate will develop deep learning methods, publish results, and collaborate across NTNU faculties. The main supervisor is Associate Professor Massimiliano Ruocco; English proficiency and strong ML background are required.