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Field
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collaborative setting that emphasized functional genomics and high-dimensional omics data analysis. The lab is a part of the NGI, home to experts in genetics, multi-omics, machine learning, and clinical research
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning applied to natural language processing, computer
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basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed
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policies have to a large degree focussed on technology shifts to bring about the required emission reductions. However, next to technology, behavioural change is crucial to achieve climate goals. Social and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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3) machine learning/deep learning, and (II) a related topic (e.g. Big Data or computing). About the Department The University of Minnesota School of Public Health improves the health and wellbeing
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at Université Paris-Saclay (https://cvn.centralesupelec.fr/ ), Prof. Pock from the Institute of Computer Graphics and Vision at Graz University of Technology (ICG ), Prof. Thiran from the EPFL Signal Processing
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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analysis methods, including quantitative structural analysis, network-based functional analysis, voxelwise cross-modality analyses, graph theoretic approaches, and machine-learning based applications