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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 19 hours ago
. Experience with technical software such in the fields of design, construction and/or building operations. Including programs such as: Dropbox, Microsoft Office Suite, 3D Modeling, and willingness to learn new
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
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models, and ensuring students have a structured and engaging learning experience. Career Readiness Competencies: Access & Opportunity Leadership Professionalism Essential Functions Teach assigned courses
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to apply Website https://www.academictransfer.com/en/jobs/359291/postdoc-in-machine-learning-and… Requirements Specific Requirements We will base our selection on the following components: a PhD degree in an
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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modelling, multimodal neuro-imaging and physics-informed machine learning to improve assessment of glioblastoma treatment response. The candidate will also be expected to contribute to the formulation and
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, designing, implementing, and evaluating ML models that address practical challenges across domains. The researcher will contribute to the development of a full machine learning pipeline, including data