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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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new MRI techniques for motion-robust imaging, real-time image processing, and/or deep learning. The work includes MRI pulse sequence design for various MRI techniques and technologies, algorithm and
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” skills coupled with in silico data analysis and QC skills. Deep understanding of molecular protocols and capacity to “tear down” protocols, identify opportunities for improvement, and development
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simulations of PDEs, deep learning, neural networks. Our research interest: Our focus is on theoretical and computational biological physics, ranging from the study of molecules to cells. We strive to leverage
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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associate will also provide leadership in coordinating different projects and advising more junior lab members. The current and prior work of the lab include deep learning algorithms for detection
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the Neurosurgical Department at the University of Iowa (with whom we perform intracranial LFP recordings from deep-brain regions as well as sEEG), the Neuropsychology Group in the Department of Neurology (which
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the Pytorch library and running deep learning models. The successful candidate will work closely with a team of researchers and faculty members in the ClinicalNLP lab led by Dr. Hua Xu. More information of the
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about