PhD Candidate: ML for Temporal & Spatiotemporal Data

Ntnu; Trondheim; Hybrid; NOK 522,000 - 638,000; Full time

Benefits offered by this job

HPC resources access

Free Norwegian language training (A2)

Open and inclusive working environment

Interdisciplinary PhD team

Job summary

NTNU in Trondheim invites applications for a four-year PhD Candidate position in machine learning for temporal and spatiotemporal data. The research will explore forecasting under distribution shifts on graphs, with applications to transport networks within STRIDE.

The main supervisor is Associate Professor Massimiliano Ruocco, co-supervised by STRIDE team members. The position requires a Master's in CS/ML or related, strong DL background, and proficiency in Python with PyTorch or JAX.

Qualifications

• Master's degree in computer science, machine learning, statistics, applied mathematics, physics or equivalent with a major independent project (minimum 30 ECTS).
• Strong English language skills (oral and written).
• Strong background in deep learning with solid probability, statistics and linear algebra.
• Proficient in Python; experience with PyTorch or JAX.
• Experience with transformers, graph neural networks, time-series forecasting or anomaly detection preferred.
• Provide link to GitHub/GitLab or similar.

Responsibilities

• Complete the doctoral education until obtaining a doctorate.
• Carry out research of high quality within the described framework.
• Develop, implement and evaluate deep learning methods and release code and benchmarks openly.
• Publish academic work and disseminate knowledge.
• Collaborate in STRIDE and with other PhD candidates in the project.

Skills

Python programming

PyTorch or JAX

English proficiency

Deep learning

Probability & statistics

Linear algebra

Education

Master's degree in CS/ML or related

Tools

GitHub / GitLab

Job description

NTNU in Trondheim invites applications for a four-year PhD Candidate position in machine learning for temporal and spatiotemporal data. The research will explore forecasting under distribution shifts on graphs, with applications to transport networks within STRIDE.

The main supervisor is Associate Professor Massimiliano Ruocco, co-supervised by STRIDE team members. The position requires a Master's in CS/ML or related, strong DL background, and proficiency in Python with PyTorch or JAX.