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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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, behavioral, clinical, genetic, and proteomic data. Prepares documentation of existing and newly developed brain image analysis pipelines, algorithms, and quantitative methods. Assists the team leader in
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machine learning, modeling algorithms, and/or mapping applications. Applicants must have a PhD in ecology, wildlife sciences or geospatial modeling. Additional Information: Salary Information: Commensurate
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communication theory, signal processing, and wireless communications, • Extensive experience in physical (PHY) layer algorithm design and performance analysis, • Proven track record
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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maintaining the DataSpaces (https://dataspaces.sci.utah.edu/download/ ) project – a high-performance distributed data management framework designed for in-memory data staging and coordination in scientific
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methods already make it possible to observe neuronal activity during photoreceptor stimulation in genetically modified animals. These methods, with further developments, could effectively provide
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genetics and genomics, with expanded interests in computational biology, functional genomics, and neuroscience. Example projects within the university and with external partners: • Noncoding Variation in
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a
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or expression, genetic information, disability, or veteran status consistent with UW Executive Order No. 81 . Benefits Information A summary of benefits associated with this title/rank can be found at https