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and graduate students in disciplines relevant to chemical risk assessment (e.g., toxicology, chemistry, endocrinology, AI/machine learning) and governmental staff presently involved in chemical risk
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machine learning and analytical models to enhance the sustainability of agricultural supply chains, with a particular focus on the Prairie region. Education: A PhD in a relevant discipline such as business
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of this position may include: Single-cell RNA-seq, perturb-seq, and/or other transcriptomic analysis Next generation sequencing and bioinformatics analysis Machine learning/AI Analyzing data and presenting the data
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that change! Qualifications The position requires a PhD degree in electrical, computer or biomedical engineering, computer science, or a closely related area. The successful candidate is expected to develop
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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contribute to the collaborative TQT research community. Principal Investigator: Na Young Kim Project Name: Solid-state analog Optimization Solver and Quantum Machine Learning (Theory) Research Area