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Field
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) years of related work experience. Experience developing machine learning methods. Experience writing, submitting, and editing peer reviewed publications. Expertise in computer science applications
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., transcriptomics, proteomics, metabolomics) and artificial intelligence/machine learning (AI/ML) applications in biomedical research will be considered a strong advantage. Outstanding UA benefits include health
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robotics, and human-machine interfaces. We are looking for an ambitious researcher who is looking to use this opportunity to grow towards the next level of research. The position is renewable each year based
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genome-based influenza and/or SARS-CoV-2 risk assessment algorithms, designing broadly protective influenza and SARS-CoV-2 vaccines using Machine Learning and Artificial Intelligence, modeling the impacts
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students, and may also teach one course per year for the Department of Statistics. A Ph.D. in Statistics, Biostatistics, Machine Learning, or a directly related field at the time of appointment is required
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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work will focus on problems in controls, machine learning, image reconstruction, wavefront sensing, and instrument development and test. As time permits, you will be encouraged to conduct your own
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for design consideration. Irradiated mechanical property prediction models and property correlation metamodels will be developed considering traditional and machine learning approaches. Extrapolation will be
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and machine learning based software to assist clinical workflow and pre-clinical studies. Recent software developed from the group has been adopted in the clinic and preclinic labs. The scientific