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University of California, Los Angeles | Los Angeles, California | United States | about 21 hours ago
experience working with human immune system and tumor models in mice (xenograft or syngeneic). • At least 5 years of post-graduate research experience in immunology, biochemistry, molecular biology, cell
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exploring multi-functional and sustainable polymers for additive manufacturing in robotics. SUME (https://www.vubtechtransfer.be/sustainable-materials-technology ), is the sustainable materials engineering
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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demonstrating the effectiveness of psychosocial support interventions and aims to develop a scalable model of care to improve mental health outcomes among breast cancer survivors. Working closely with the UK
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, imaging, cell sorting, and next-generation sequencing, in conjunction with innovative mouse and stem cell models. In the Han lab, you will gain hands-on experience with these techniques and have the
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to Predict Spectra of PFAS Compounds The work will entail: The Materials Measurement Laboratory of the National Institute of Standards and Technology is seeking qualified persons (U.S. Citizens preferred
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of thermal degradation. This research has enabled the development of effective kinetic models, based on parallel or sequential reactions. However, the actual operating conditions of pellet stoves
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trips, plus an elective course. Examples of elective courses could include aqueous geochemistry, geomorphology, soils, glaciology, GIS, or numerical modeling. We are seeking candidates who can demonstrate
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characterization of polymeric and carbon-based materials Investigation of their catalytic activity using model molecules to gain fundamental understanding of the system Translation of these findings toward catalytic
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statistical models to understand and explain biological phenomena. • Create algorithms to integrate large and diverse datasets into theoretical frameworks for modeling complex biological systems. • Collaborate