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opportunities to learn new techniques. The chance to contribute to publications and gain authorship credit. A strong foundation and preparation for graduate school or PhD programs. A collaborative and supportive
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Science at MBZUAI focuses on the rigorous statistical and probabilistic foundations of machine learning and data science. We emphasize computational methods for large-scale data and scalable inference
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such as causal inference or machine learning or complex panel data analysis. We are seeking excellent applicants with an international research portfolio and network. The teaching portfolio includes courses
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time in response to portfolio needs Nature Machine Intelligence publishes high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. We also explore and
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation
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to systematically understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, including machine learning, deep learning
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operation of the laboratory equipment and day to day running of the microwave pyrolysis laboratory Your profile The applicants should hold a PhD in applied chemistry, chemical engineering or similar. Both
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Yale Center for Brain & Mind Health (CBMH) Faculty Position: Artificial Intelligence in the Promotio
integrate, advance or develop approaches such as natural language processing, machine learning, computer vision, foundation models, large language models, and other methods. Applicants may work in one
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote