19 phd-phd-control-engineering-"https:" Postdoctoral positions at Pennsylvania State University
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. Qualifications PhD, MD, or MD/PhD in genetics, stem cell biology, molecular biology, biomedical engineering, or related discipline. Experience in iPSC culture and differentiation. Experience with genome editing
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-electron microscopy–based approaches. More information about the laboratory can be found at: https://www.cryo-em.psu.edu/ . The postdoctoral scholar will contribute to ongoing projects to define structural
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of head and neck squamous cell carcinoma (HNSCC). The overarching goal of the research program is to identify biologic determinants of tumor control and normal tissue injury following radiation therapy, and
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and contribute to grant development Present findings at laboratory meetings and national/international conferences Mentor graduate students and junior trainees as appropriate Required Qualifications PhD
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AML using CDX, PDX, and genetically engineered mouse models (GEMMs) Perform in vivo imaging, tissue harvesting, flow cytometry and molecular analyses Maintain detailed experimental records and rigorous
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graduate students and research trainees as appropriate. Qualifications PhD, MD, or MD/PhD in cardiovascular biology, physiology, molecular biology, or a related field. Experience in patch-clamp
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holding a PhD degree in Biomedical Sciences or a related field are eligible to apply. Life sciences (Biology, Genetics, Biochemistry, etc.) PhDs with computational experience are also eligible
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of designing hypothesis-based experiments, executing studies, and preparing manuscripts. Requires a PhD in Biochemistry or related field; significant experience in molecular biology, biochemistry, and mouse
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, computer science, physics, or engineering by the start date of employment. Strong expertise in one or more of the fields of scientific computing, data science, and quantum computing accompanied by strong programming
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Professor John Harlim and his collaborators, Yan Li in the Electrical Engineering department, and Daning Huang in the Aerospace Engineering department in the area of Scientific Machine Learning. The project