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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high
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genetically diverse mice under a multitude of conditions, and (3) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing
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/2026 Position Qualification Position Summary Developing and/or applying artificial intelligence, machine learning and/or data science-based methods to address cutting-edge topics, including but not
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seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new
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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures
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segmentation, anomaly detection, and extraction of biomarkers for disease classification. Design deep learning models for multimodal image registration and alignment of ultrasound with ground-truth MRI data. 2
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sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform both
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and