153 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Nature Careers
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based
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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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a skilled Data Scientist with a strong foundation in genomic biostatistics to join our team. This role involves leveraging advanced statistical methods and machine learning techniques to analyze
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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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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience
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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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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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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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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