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Field
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: Deep proficiency with modern machine learning, including deep learning, transformers, graph neural networks, generative models, and foundation models and their application across biomedical and chemical
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and application of AI/machine learning methods and statistical modelling for the analysis of biological and medical data. Demonstrated ability to communicate and present scientific research findings in
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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European Molecular Biology Laboratory (EMBL) | Brandenburg an der Havel, Brandenburg | Germany | about 2 months ago
machine learning/AI with modern biology at the AI Hub Heidelberg as part of EMBL AI, a major institutional initiative to embed AI across all areas of life sciences. EMBL provides a unique environment for
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, applications, tools, and services for the broader research community to use data at scale to pursue scientific inquiry and accelerate discovery. Learn more at https://gdc.cancer.gov/, https://gen3.org/, https
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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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one or more of the following areas: Programming, Algorithms and computational complexity, Programming languages and compilers, Computer graphics, and Parallel and GPU computing. A typical teaching
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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
machine learning, multidimensional inversion, and probabilistic geological modelling to enable efficient mapping in previously inaccessible terrains. The successful candidate will be employed primarily in
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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applications for a Research Associate position in the area of efficient and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data