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research team dedicated to advancing our understanding of migraine, pain mechanisms, neuroimmune interactions, and nervous system function. Your expertise, curiosity, and commitment to scientific discovery
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testing hypotheses and analysing scientific data from a variety of sources, reviewing and refining working hypotheses as appropriate. You will develop research questions, analyse detailed and complex
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months. The project involves the synthesis of molecular machines capable of interfacing with lipid bilayer membranes, including mechanically interlocked molecules and shuttles; and systems with
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system surrogates, or uncertainty quantification. Model Predictive Control (MPC), data-driven control, or hybrid model-based / data-driven controller synthesis (e.g., RL-MPC) for complex dynamical systems
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experience is welcome but is not required. Duties and Responsibilities Design and conduct independent and collaborative studies of phiBB-1/cp32 biology, phage replication, lysis-lysogeny regulation
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expertise in inorganic and organometallic chemistry since heterometallic complexes are targeted. Polymerization catalysis kinetics will be investigated using specialist reactors, in situ spectroscopy
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University of Southern California - Center for Craniofacial Molecular Biology | Los Angeles, California | United States | 2 months ago
This is a grant funded, fixed-term position (eligible for renewal) The Center for Craniofacial Molecular Biology (CCMB) at USC’s Herman Ostrow School of Dentistry is recruiting a talented
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A postdoctoral research fellow position is available in the lab of Dr. Charleston Chiang at USC. The Chiang lab( opens in new window) utilizes cutting-edge analytic tools to address questions
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precision medicine tools for these conditions. We have a track record of training outstanding postdocs who go on to have successful high impact careers in academia and the private sector. This is an
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA