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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
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research activity. In this project, you will develop fundamental machine learning methods and apply them in an interdisciplinary research environment spanning physics, neuroscience and computational science
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the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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representations developed in them as a foundation for this research activity. In this project, you will develop fundamental machine learning methods and apply them in an interdisciplinary research environment
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: OUH - Cell and tissue dynamics (Bøe) Project description GENESIS is a newly established Life Science Convergence Environment that brings active matter physics, cell biology, and machine learning
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast
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of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and large language models for measurement challenges (e.g
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of Computer Science and affiliated with the Information Systems and Human–Computer Interaction (ISCHI) research group. Your immediate leader will be the unit leader of the Information Systems and Human–Computer
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Science Convergence Environment that brings active matter physics, cell biology, and machine learning to address the fundamental processes guiding the earliest stages of mammalian embryo development. Early