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Massimiliano Ruocco

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PhD Candidate in Machine Learning for Temporal and Spatiotemporal Data Norwegian University of Science and Technology in Norway

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Nov 20, 2026

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Country

Norway

University

Norwegian University of Science and Technology

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Keywords

Computer Science
Information Technology
Deep Learning
Mathematics
Forecasting
Anomaly Detection
Resiliency
Statistics
ML

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About this position

This PhD project at the Department of Computer Science, Norwegian University of Science and Technology (NTNU) focuses on machine learning for temporal and spatiotemporal data, including time series and multivariate time series observed on graph nodes and edges. The research is centered on forecasting and on detecting disruptions in data drawn from complex networked systems.

The position is embedded in STRIDE, an interdisciplinary NTNU project on the resilience of transport systems. Transport networks are the main testbed, and the work will study how learning methods can remain reliable when networks are pushed into rare or previously unseen operating regimes. The project aims to develop deep learning approaches that can both forecast under distribution shift and detect or locate disruptions from observed data.

Possible research directions include time-series foundation models, pretraining on synthetic data from simulated dynamical systems, spatiotemporal graph neural networks that model how disruptions propagate through a network, and anomaly detection/localization in time series on graphs. The exact focus will be shaped together with the successful candidate, with an expectation of publication in leading machine learning conferences and journals.

The PhD candidate will work in the Data and Artificial Intelligence (DART) group and collaborate closely with other researchers in the STRIDE team. The wider project connects transport modelling, artificial intelligence, operations research, civil engineering, and science and technology studies, creating an interdisciplinary environment around societal resilience and transport-system robustness.

The main supervisor is Associate Professor Massimiliano Ruocco, with co-supervisors from the STRIDE team. The role includes research, publication, open release of code and benchmarks, participation in international activities, and one year of teaching or other career-promoting work at NTNU.

Funding details

Available

What's required

Applicants must meet admission requirements for the PhD programme in Computer Science and hold a relevant Master's degree in computer science, machine learning, statistics, applied mathematics, physics or equivalent, including a major independent project of at least 30 ECTS equivalent to a master's thesis. Master students may apply if the degree is completed and documented before starting and no later than 1 August 2027. A strong academic record is required, with an average grade of B or better according to NTNU's grading scale, or an equally strong academic foundation if no letter grades are used. Candidates must have good oral and written English, a strong background in deep learning, solid foundations in probability, statistics and linear algebra, good programming skills in Python, and experience with PyTorch or JAX. Preferred experience includes transformers and foundation models, graph neural networks, time-series forecasting, anomaly detection, and evidence of implementing and training deep learning models (with code links such as GitHub/GitLab).

How to apply

Apply electronically via Jobbnorge.no. Include transcripts and diplomas, CV, a copy of the master's thesis or draft, a research statement, a short motivation letter, publications or other relevant work with code links if available, and contact information for three referees. If applicable, include documentation of foreign education and any HK-dir statement.

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Professors

Associate Professor at Norwegian University of Science and Technology

University Name
Country flag

Norway

Research Interests

Statistics

Deep Learning

Information Technology

Resiliency

Mathematics