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into the transmission X-ray imaging regime. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning, computational imaging, and/or computer vision
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Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status
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as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the project Can AI interpret graphs like a human materials scientist
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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31 Jul 2026 Job Information Organisation/Company Computer Vision Center Research Field Computer science » Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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) for Future Distribution Networks. The position is for a period of three years. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme
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/PhD in Geosciences, Environmental sciences, remote sensing or computational sciences or image vision and related fields Language skills: Excellent English skills, German is beneficial Desirable skills
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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priorities and learn new skills as needed. Office-based role with occasional events requiring evening or weekend hours. Frequent use of computer systems and software, including social media and marketing tools