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Field
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-on expertise in building and characterizing 3D multicellular tissue models — including hiPSC-derived and primary-cell co-culture/organoid systems — to lead studies defining how spatial and multicellular context
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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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. Responsibilities will include electromagnetic simulation and modeling, RF coil development and fabrication, 3D design and printing of mechanical components, laboratory testing, and evaluation of prototype systems
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Instrumentation Resource Facility (IRF) within the Office of the Vice President for Research. The Core will provide fee-for-service, collaborative research support, and instrument access for organoid models
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method Experience from using 3D clinical images to derive computational modelling geometries Experience from tissue musculoskeletal biomechanics Experience from studies of mechanobiolgical modelling
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delivery performance in relevant in vitro 3D tissue models. The project combines nanoparticle formulation, colloidal self-assembly principles and advanced physicochemical characterization with cellular and
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processes. Experience with the Microsoft suite of applications, photo/video editing software, and design tools such as the Adobe Creative Suite and 3D modeling software (Rhinoceros3D, Fusion360, SolidWorks
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grant and/or designated source and therefore continuation in the position is contingent upon funding availability. Key Responsibilities • Physics & Kinematic Modeling: Develop and validate real-time
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outcomes. Research Data Management and Analysis: Lead the processing, cleaning, integration, validation and analysis of complex clinical and skin imaging datasets, including 2D and 3D medical imaging data
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to apply state-of-the-art Embodied AI, including Vision-Language-Action (VLA) models and Diffusion Policies, to real-world robotic manipulation. Key Responsibilities: • Develop and implement multi-modal