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twins, optimization, and control. In this PhD project, you will develop a new systems and control theory for learned operators, bridging modern scientific machine learning with classical control theory
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to the assessment and optimization of indoor and outdoor environmental conditions affecting health and comfort in urban areas, including air pollutants, thermal stress, humidity, solar radiation, and wind flow. The
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develop and optimize scientific and engineering applications leveraging high-speed network capability provided by the Energy Sciences Network or run on next-generation high performance computing and data
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modeling. Integrate and evaluate automatic segmentation algorithms within a reproducible anatomical-modeling workflow. Refine segmented geometries and optimize surface and volumetric mesh generation
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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controllers for complex systems that are safe and verifiable by design. Information Neural networks can provide the flexibility needed to control increasingly complex dynamical systems, but their opaque and
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computing ecosystems Proven experience in developing and applying deep learning methods for image analysis, such as convolutional neural networks (CNNs) for segmentation and classification Practical
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high availability, backup/recovery, and security. Lead performance tuning and optimization, including SQL tuning, index strategies, statistics management, query plan analysis, and configuration tuning
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ArcGIS Experience Builder, Dashboards, Survey123, and related tools. Administer and optimize enterprise geodatabases, including SQL views, schema design, privileges, versioning, and replica management
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, satellite or aerial imagery, road networks, building footprints, utility features, and other digital maps. The research will emphasize spatial accuracy, confidence estimation, and uncertainty-aware mapping