148 machine-learning-"https:" "https:" "https:" "https:" "https:" positions at Nature Careers
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/performance trade-offs and typical RAN levers; experience with energy metering data is a plus. • Strong background in AI / Machine Learning for decision-making (e.g., forecasting, optimization with learning
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Assistant Professor of Machine Learning in Digital Health (salary group W1
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innovative research in three major categories: AI Fundamentals: machine learning, deep learning, data science; AI Core Applications: computer vision, natural language processing, speech processing, robot
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. Describe a deep learning project you have executed. Projects in computer vision for microscopy image analysis are especially relevant. Include a link to a code repository if possible. If you contributed to a
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Profile Responsibilities Design and implement analysis pipelines for high-throughput microscopy and sequencing data Develop novel image-analysis and machine-learning approaches for quantitative cell biology
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the synaptic wiring diagram, or connectome, of whole brains, designing, building and using machine learning approaches to computer vision as applied to volume electron microscopy of whole brains. The ideal
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the University Commission, UW invites applications for three (3) tenure-track Assistant/Associate Professor positions in applied artificial intelligence (AI), machine learning (ML), and advanced applications of AI
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if they demonstrate strong relevant skills. Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming. Exposure to machine learning / data-driven modelling and/or
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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities