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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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by logging into their existing Workday employee account. Position Summary: This position will focus on developing robotics and cyber physical systems solutions, using machine learning and artificial
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on innovative design for legal and regulatory systems for AI and other complex global technologies; computational models of human normative systems; and working with machine learning researchers to build ML
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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes
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-dimensional data, survival and event history analysis, model selection and criticism, graphical modelling, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
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cerebral organoid models to study the impact of environmental exposures on metabolostasis and proteostasis Build and validate machine learning models (in collaboration) for molecular data analysis and
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approaches will include species distribution models and integration of spatially explicit environmental variables including climate, hurricane disturbance, and land-use, among other global and Caribbean region
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are an NSF-funded research project. The primary purpose of the position is to build a digital twin for national-scale field-level crop monitoring, modeling, prediction, and decision support and automation