PhD Research Fellow in Machine Learning and Statistics
Develop advanced vision-language models capable of structural visual reasoning and explainability.
Best for:Best for candidates with a strong foundation in machine learning, computer vision, and graph-based modeling.
Overview
The University of Oslo is offering a PhD Research Fellow position focused on machine learning and statistics. The research aims to pioneer a new generation of vision-language models (VLMs) that perform genuine visual reasoning over images rather than merely providing descriptive text. Successful candidates will design models that are more accurate, explainable through graph structures, and applicable across robotics, medical imaging, and autonomous driving. The candidate is expected to conduct independent research, publish findings in top-tier venues, and contribute to the academic environment of the department.
What you'll do
- Design and implement novel vision-language architectures that perform structural reasoning over visual data.
- Develop graph-based representation methods to enhance model explainability and traceability.
- Conduct empirical evaluations across domains such as robotics, medical imaging, and autonomous driving.
- Publish research findings in premier international machine learning and computer vision conferences and journals.
- Collaborate with interdisciplinary research teams and contribute to academic activities within the department.
Requirements
Degree required: Master's degree in Computer Science, Statistics, or a related quantitative field
Degree offered: PhD
Language: English; strong written and verbal proficiency
Instruction: English
- Strong background in machine learning and deep learning methodologies
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience with computer vision or natural language processing tasks
- Solid foundation in statistics and applied mathematics
- Must hold a relevant Master's degree before taking up the position
Funding & terms
How to apply
Deadline: 2026-11-18
Interested applicants must submit their application through the designated external portal link provided. Ensure all required documentation including academic transcripts and CV are uploaded prior to the deadline.
- CV
- Cover letter
- Academic transcripts
- Master's thesis
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