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of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep learning and the use of robust statistics. This work is naturally extended to studying
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics
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and dissemination of research findings. Example Projects: Developing deep learning models to analyze spatial single-cell profiles. Using large language models (LLMs) to extract information from
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, aiming to transform the care for patients with abdominal aortic aneurysms (AAA). You will develop and validate cutting-edge multimodal deep learning models that integrate imaging and clinical data
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experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large Language Models Time-Series Data Prediction and Modeling Intelligent
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students. Qualifications: Required: PhD in Computer Science, AI, Data Science, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs
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of Oslo. via Unsplash Main responsibilities The position is fully embedded (100%) within SmartForest. The main responsibilities include: Developing supervised deep learning algorithms for 3D laser data
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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postdoctoral researcher with a focus on AI trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on
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Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application