Norwegian Institute of Science and Technology
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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
This is a four-year full-time position comprising three years of doctoral work and one year of career-promoting work. The gross salary is normally NOK 580,000 per year, depending on qualifications and seniority, with a 2% statutory contribution deducted for the State Pension Fund. Benefits include access to high-performance computing resources and GPUs, working capital for the project, career guidance, a mentor programme, pension membership, and other NTNU employee benefits.
Deadline
Nov 20, 2026
Country
Norway
University
Norwegian Institute of Science and Technology
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About this position
PhD Candidate in Machine Learning for Temporal and Spatiotemporal Data
The Department of Computer Science at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, invites applications for a four-year PhD position in machine learning. The project is part of STRIDE, an interdisciplinary initiative studying the resilience of transport systems under disruptions.
The research will develop deep-learning methods for temporal and spatiotemporal data, including multivariate time series observed on graph nodes and edges. Key themes include time-series forecasting under distribution shift, time-series foundation models, synthetic-data pretraining, spatiotemporal graph neural networks, and anomaly detection and localization of disruptions. Transport networks will provide the primary testbed.
The successful candidate will join NTNU's Data and Artificial Intelligence (DART) group, collaborate with researchers across transport modelling, artificial intelligence, operations research, civil engineering, and science and technology studies, publish in leading venues, release code and benchmarks openly, and participate in conferences or research stays. The appointment includes three years of doctoral research and one year of career-promoting work, including teaching.
Applicants need a relevant Master's degree with a substantial independent project, normally a B average or better, strong preparation in deep learning, probability, statistics and linear algebra, English communication skills, Python programming, and experience with PyTorch or JAX. Experience with transformers, foundation models, graph neural networks, forecasting, anomaly detection, or public research code is advantageous.
The salary is normally NOK 580,000 gross annually, subject to qualifications and seniority, with access to NTNU computing resources, GPUs, career support, a mentor programme, pension benefits, and other employee benefits. The application deadline is 20 November 2026. Applications must include academic transcripts and diplomas, CV, Master's thesis or draft, research statement, motivation letter, relevant publications or code, and three referees.
Funding details
This is a four-year full-time position comprising three years of doctoral work and one year of career-promoting work. The gross salary is normally NOK 580,000 per year, depending on qualifications and seniority, with a 2% statutory contribution deducted for the State Pension Fund. Benefits include access to high-performance computing resources and GPUs, working capital for the project, career guidance, a mentor programme, pension membership, and other NTNU employee benefits.
What's required
Applicants must meet the admission requirements for NTNU's PhD programme in Computer Science and hold a relevant Master's degree in computer science, machine learning, statistics, applied mathematics, physics, or an equivalent field, including a major independent project of at least 30 ECTS equivalent to a Master's thesis. Master's students may apply, but the degree must be completed and documented before starting and no later than 1 August 2027. Applicants normally need a Master's average of B or better on the NTNU scale, or an equivalent academic foundation; candidates with weaker grades may be considered if they demonstrate exceptional suitability through relevant work experience or peer-reviewed publications. Strong oral and written English communication skills, a strong background in deep learning, solid foundations in probability, statistics and linear algebra, good Python programming skills, and experience with PyTorch or JAX are required. Preferred experience includes transformers or foundation models, graph neural networks, time-series forecasting, anomaly detection, deep-learning implementation and training, and publicly available code. Applicants should be able to work independently, communicate research, demonstrate curiosity and motivation, analyze data, collaborate across disciplines, and draw well-founded conclusions.
How to apply
Apply electronically through Jobbnorge using the direct vacancy page. Submit a CV, Bachelor's and Master's transcripts and diplomas, Master's thesis or draft, a research statement of up to 1,000 words, a motivation letter of up to 400 words, publications or other research work with code links where relevant, and contact details for three referees. Foreign-educated applicants should also provide documentation of the scope and quality of their education, including a diploma supplement where applic
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Professors
Associate Professor
Norway
Research Interests
Statistics
Ml
Computer Science
Deep Learning
Forecasting
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