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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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), primary cell culture from patient bone marrow/peripheral blood samples, PBMC isolation (density gradient centrifugation, e.g., Ficoll-Paque), and Bone marrow mononuclear cell isolation and processing
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significance of bioluminescence. We will also apply a range of advanced imaging techniques to visualize the light organs and their innervation. The successful applicant will be part of a highly interdisciplinary
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. The successful applicant will work primarily on two projects: Second Opinion Program: Evaluating a model designed to reduce unnecessary lumbar spine surgery and improve return-to-work outcomes for injured workers
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expert partners, LRAs will harness interdisciplinary scientific methods to advance a new digital coast paradigm, nature-based solutions, and social innovation. Key responsibilities and duties: As a
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design for fMRI studies including the design and implementation of experimental paradigms used in the investigation of cognitive, affective or social processes • Demonstrable experience in the analysis
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radiation in fields such as ultrafast spectroscopy, imaging, materials science and semiconductor research. HHG Beamline User Operation Actively participate in the preparation and execution of user experiments
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to their design and characterisation. • Contribute to producing the relevant procedures and software tools in order to achieve the above. • Contribute to the design of relevant prototype components and participate
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to their design and characterisation. • Contribute to producing the relevant procedures and software tools in order to achieve the above. • Contribute to the design of relevant prototype components and participate
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biofluid samples Conceive, prototype, and benchmark new computational and AI methods for liquid biopsy, including techniques to bridge to other data modalities, e.g. imaging Apply modern machine learning