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studying deformation mechanisms in refractory alloys as via atomic-scale calculations as well as application of machine learning to materials discovery The ideal candidate will have the following
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goal of Dr. Ke’s lab is to develop computational approaches and leveraging bioinformatics tools, metagenomic sequencing, multi-omics data, machine learning, and wet-lab techniques to investigate the role
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modeling, and/or machine learning models A pattern of well-regarded interpersonal collaborations (i.e., are you someone people enjoy collaborating with?) Additional Information: The Department of Psychology
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basic laboratory procedures to vital signs may be desired; computer skill required with knowledge of database software applications desired. Final candidates are subject to successful completion of a
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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Assistant will complete CITI Training and other Computer Based Learning Trainings to gain access to necessary study platforms such as Institutional Review Board (IRB), Integrated Healthcare Information System
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engineering, materials science and engineering, or computer science and engineering. The postdoctoral fellow is expected to actively work with more than one CQISE faculty member at OSU to strengthen
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advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
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in Dr. Shanlin Ke’s lab. The overarching goal of Dr. Ke’s lab is to develop computational approaches and leveraging bioinformatics tools, metagenomic sequencing, multi-omics data, machine learning, and
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. Demonstrated expertise in interdisciplinary research with technical and clinical teams and patients desired. Experience leveraging machine learning for human musculoskeletal biomechanics applications desired