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Field
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/Qualifications Strong research track record in AI and scientific applications. Excellent knowledge of Machine Learning and Deep Learning. Strong Python programming skills. Experience with PyTorch, TensorFlow
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in the area of probabilistic methods in machine learning. These include applications of Random Matrix theory and Gaussian processes to Deep Neural Nets. Teaching may be required. Education and
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
-scale deep learning, ideally applied to protein or nucleic acid sequences Ability to work independently and to translate diverse scientific use cases (brought by wet-lab and computational colleagues alike
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are seeking a highly motivated researcher with a strong background and interest in machine learning and artificial intelligence. The position focuses on the development and application of advanced deep learning
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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. deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 2 months ago
research on population coding and Alzheimer’s disease Key research methods: fMRI (including 7T), MEG, virtual reality (VR), a wide variety of cognitive tasks, psychophysics, machine learning, deep neural
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3 , N2 O, CO2 , CH4 ) using satellite observations, atmospheric chemistry models, and physics-informed deep learning