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Field
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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, and ionization cross sections using data derived from experiment and computation. - Implementing algorithms to study the performance of AI/ML classification models. - Assessing
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computer science, operations research, applied math, statistics, or a related field Strong background in machine learning, optimization, and/or algorithm design Excellent written and verbal communication skills
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creating the fastest algorithms for modeling of entanglement purification circuits; creating the highest performance quantum error detection circuits; developing symbolic computer algebra systems for quantum
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vision, controls, cyber-physical systems and their security, hardware security, and machine learning and their security. The work will include algorithm design, prototype implementation (e.g., in Matlab
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with applications to aerospace systems Designing, implementing, and testing control algorithms in simulation and hardware platforms Contributing to publications and reports; presenting research findings
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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for hypersonic wind tunnels, under the direction of Dr. Samuel Grauer. Job Duties: Include refinement of the neural-implicit LAT (NILAT) algorithm. Hardware design. Deployment at NASA facilities. The role combines
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Engineering, Computer Science or related + 2 years of experience researching multi-armed bandit problems and developing and analyzing Bayesian optimization and reinforcement learning algorithms + at least 3
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criteria on which images to further analyze. For included images, correctly apply algorithm according to techniques learned at the University of Washington with ongoing support as needed. Work closely with