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work on collaborative research projects in the Social Sciences; analyse large-volume textual data with machine learning algorithms; analyse survey and other quantitative and qualitative data; perform any
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large
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medical physics. - Knowledge about medical imaging and machine learning would be a plus. - Good practice and knowledge of programming or prototyping softwares - Willingness to get involved in the medical
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to addressing considerations like compositionality that we know to be at the heart of the differences between quantum and classical theories and phenomena; machine learning techniques, in particular
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processing techniques, supported by artificial intelligence and machine learning algorithms, to streamline the interpretation of spectroscopic data. The research will be carried out in close collaboration
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, certifications, Licensure) Demonstrated record of publishing research in quality journals. Expertise in coding. Experience with machine learning and image analysis. Strong interest in neuroscience, specifically
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Postdoctoral Researchers to work on research problems at the interface between a selection of the following areas: - Bayesian inference for deep learning - Generative modeling - Computational statistics
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(essential). Advanced experience in GIS (essential). Appropriate experience (essential) in either (1) machine learning (fully-convolutional neural networks) applied to remotely sensed data, either/or (2
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, the position is most appropriate for recent master's graduates (or soon to graduate) in fields related to machine learning, computer science, material science or related disciplines with excellent academic