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Field
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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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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Qualifications: Experience with aging populations or neurodegenerative diseases Familiarity with deep learning and advanced statistical approaches to neuroimaging data Prior publications in relevant areas Required
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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imaging pipelines, and use deep learning to gain insight into biological processes. You will also gain direct exposure to cardiovascular physiology and rodent imaging in close collaboration with biologists
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine
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. The appointee will primarily conduct research applying advanced machine learning/AI (including techniques like deep learning) to analyze complex biological and clinical data (e.g., single-cell multi-omics
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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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stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key interdisciplinary areas