54 phd-in-mathematical-modelling-population Postdoctoral positions at Pennsylvania State University
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, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine learning) or a directly related field at the time of appointment is required. The successful applicant
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—biochemistry, biophysics, omics, and transgenic mouse models—to deepen our understanding of calcium signaling under both physiological and pathological conditions, including fibrosis and cancer progression. Our
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows, and deep learning. A Ph.D. in Statistics, Mathematics, CS/EE (with a focus on statistics/machine
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to investigate iconicity in spoken language – the idea that the sound of a word may convey its meaning – in both neurotypical people and people with aphasia. The successful candidate will have a PhD in a relevant
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functional magnetic resonance imaging: fMRI) methods to investigate iconicity in spoken language – the idea that the sound of a word may convey its meaning. The successful candidate will have a PhD in a
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resonance spectroscopy applied to investigate post-translational modifications of intrinsically disordered proteins. The position requires a PhD in chemistry with a focus on spectroscopy applied
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project management. This position requires a PhD in nematology (preferred), forestry, plant pathology, microbiology, microbial ecology, or a related field. Competitive candidates must have significant
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, Mathematics, or a closely related field. For full consideration applicants must complete the Penn State application and must submit the following materials by May 15th. • A research statement. • Curriculum
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research involving biological data analysis and modeling of biological systems. In particular, they will develop and apply algorithms to construct discrete dynamic models of signal transduction networks