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. Experience with both experimental and computational approaches is desirable, as is proficiency in widely-used microbiological methods. The Aylward lab is a vibrant and dynamic work environment that welcomes
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performance in advanced nuclear reactor environments. Under the mentorship of the Center Director/PI, the postdoc will develop and apply novel experimental methods, advanced characterization techniques, and
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remaining. ● Strong background in the development and/or application of numerical methods for partial differential equations. ● Experience in implementation of numerical methods. ● Interest and experience in
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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opportunities for scientific growth. The group has significant experience transitioning lab members to academic and industrial positions. Interested candidates should send their resume, statement of research
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, optimization and machine learning. • Ability to pursue independent research and demonstrated capability of mentoring students. • Strong background and experience in network optimization and evaluation especially
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publications • Experience and background in managing multiple projects simultaneously • Excellent communication skills Preferred Qualifications • Experience with induced primary, cancer and pluripotent stem
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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. The Postdoctoral Associate is expected to lead research projects from experimental design, data analysis, to presentations in lab meetings, conferences, and seminars, as well as prepare manuscripts for peer-reviewed
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concurrent duties - Excellent communication skills - Strong track record of publishing in peer-reviewed journals Preferred Qualifications - Proficiency with data analysis and visualization in R - Experience