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Field
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the NIH and NSF. Mentor and help recruit and train graduate students and undergraduate researchers. Design and conduct experimental studies. Present research findings at national and international
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solid electrolytes. We are seeking candidates who will be able to design experiments and develop methodologies to design material compositions, ink rheological properties, and coating and drying process
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originality Experience in designing, running, and analyzing data from image-based physiology and single-cell activity profiling Preferred Qualifications: Experience with cultivation techniques for plant, fungal
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onsite. The work is performed on-site or at a designated assignment location. This position is for one year with extensions contingent on continued funding, performance, and needs of the lab. Be Bold
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the last five years. A minimum of 3 years of experience in (a) design and operation of experimental thermal/fluid systems, (b) mechanical systems and instrumentation, (c) fluid dynamics, combustion, and heat
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 17 hours ago
stronger and we’re united in fostering inclusivity in all aspects of our research to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future." In your
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 2 months ago
seeks a faculty collaborator to engage in projects with the Research Innovation Center (RIC) at the National Energy Technology Laboratory (NETL) in the area of Innovative Concepts under the mentorship
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. Research Environment The Kebriaei Laboratory is committed to fostering a collaborative, inclusive, and supportive research environment. Trainees receive mentorship in experimental design, scientific
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partners on study design and result interpretation. Maintain reproducible computational workflows and well-documented analysis pipelines. Present research findings at laboratory meetings, scientific
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral