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multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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of computer vision models is comfortable working in multidisciplinary research environments, where methods are applied to biological or real-world problems Qualifications The applicant must: hold a PhD in a
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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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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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. Machine learning algorithms and physics of failure modelling LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelBasic Additional Information Benefits MSCA Postdoctoral Fellowships enhance the creative and
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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Degree or equivalent Skills/Qualifications Technical Skills Python. C++. C# Java. SQL Scikit-learn. Specific Requirements Knowledge Artificial Intelligence Models. Machine Learning. Data Management