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management, cloud computing, machine learning, and algorithms for the Internet. Example topics of interest include but are not limited to the design and analysis of sketches and filters for use in real systems
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
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Predication and discovery of new materials for next generation solar cells driven by machine learning. Demonstrated academic research experience by publications in high quality research journals. At Level B
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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the Sterne-Weiler Lab i n Computational Biology / Discovery Oncology and co-mentored by the Frey Lab in Prescient Design (Machine Learning for Drug Discovery). The postdoctoral position is focused
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at CERI under supervision of Dr. Goebel. and should contribute broadly to induced seismicity research. Specific research topics include seismicity analysis, machine learning, reservoir modeling, geothermal
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pathology on computational, molecular, cellular, preclinical and translational levels. A spectrum of scientific methods includes state-of-the-art multi-omics approaches, machine learning and implementation
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: Strong understanding of statistics, probability, optimization, and linear algebra. - Machine Learning: Deep learning, probabilistic modeling, generative models, etc. - Programming & Software Development