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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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, including: Surrogate models or learned potentials Generative models for biomolecular design Representation learning for biomolecular systems Familiarity with protein–protein interaction (PPI) networks
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success Basic Qualifications: A Ph.D. in Condensed Mather Physics, Materials Science and Engineering, or
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. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Design system-level approaches for time-sensitive or data-intensive processing of data originating
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AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi-modal datasets. Develop AI/ML approaches to bridge length- and time-scales in
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characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating
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Qualifications: Ph.D. in electrical, electronics, or VLSI engineering; computer engineering; computer science; computational science; or a closely related field completed within five years of application Strong
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one