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need, this may be prepared from published field operation data, laboratory measurement or other sources. Machine learning can be used to select the bet data set for each particular cases covering
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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settings, and (IV) frequent production of too many equally fitting models. The position will involve leveraging AI techniques custom-built for processing high-dimensional, noisy data to help CNA overcome
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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decision tool based on SLP/NLP, and utilizes large language models. The project will focus on interaction with clinicians, with a goal of closing the gap between foundational research in machine learning and
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of Informatics, Uni-versity of Oslo, and will be part of a growing research agenda at the intersection of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns
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skills in statistical analysis and mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral
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background in one or more of the following fields are required: Numerical solution strategies for PDEs Mathematical modelling. Furthermore, experience within machine learning, parameter estimation/inverse
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-making. However, traditional machine learning models face limitations in this domain due to several critical challenges. First, ICU data are high-dimensional and multimodal, with patient states evolving
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of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns with recent developments at the HISP Centre at UiO, which is expanding its long-standing DHIS2