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activities beyond the project duration. ESSENTIAL REQUIREMENTS: Holder of PhD title (food science, food technology, food biotechnology) or its equivalent Fluency in written and spoken English (the working
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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strong asset. Qualifications: A PhD or equivalent qualification in a relevant field, such as marketing, consumer behavior, psychology, behavioral economics, sustainability science, or related social
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally
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electrocatalytic processes. This postdoctoral position will investigate pulse-mediated electrodeposition of metal particles using deep eutectic and organic solvents, ultimately aiming to induce kinked high-index
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and