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Field
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multi-omics, tumor immunology, and in vivo genetically engineered mouse models. By bringing these approaches together, we strive to generate insights that will translate into better therapies and improved
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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immunity and prevent pulmonary diseases. The research will use both in vitro (air-liquid interface cultures, organoids, and organ-on-a-chip) and in vivo (transgenic and knockout mice) model systems
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
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researchers, to translate findings across model systems and human cohorts. Mentor junior lab members, including graduate students and research staff, and contribute to fostering a supportive and rigorous
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Experimental experience in academic research lab environment Design, fabrication, and characterization of MEMS devices and systems Cleanroom experience is required Preferred Qualifications Modeling and
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modeling, control, and optimization of power and energy systems with applications to maritime and coastal infrastructures (e.g., shipboard microgrids, port facilities, islanded communities, desalination
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refinement of innovative research methodologies and will independently design, optimize, and execute complex immunologic and molecular experiments across in vitro, ex vivo, and in vivo model systems. Technical
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genomics, with a focus on genomic discovery, high-dimensional modeling, and precision medicine approaches for primary open-angle glaucoma. Responsibilities include management of all aspects of computational