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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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on histopathology image data. As a postdoctoral researcher you will be involved with development and validation of AI/deep learning solutions for precision medicine and patient stratification (patient outcome
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central area of expertise. The successful candidate shall demonstrate deep knowledge of LCA methodology and tools, and show strong competencies in methodological development and application across various
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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chemometrics, machine learning, or deep learning, particularly for classification, clustering, or pattern recognition in large datasets. Proficiency in Python, MATLAB, or similar platforms used for image
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learningPrior experience in deep learning, or/and