80 phd-position-in-source-function "AAAS" Postdoctoral positions at Oak Ridge National Laboratory
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participate creatively in collaborative teams across the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt
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for extension. Initial appointments and extensions are subject to performance and availability of funding. Please submit three letters of reference when applying to this position. You may upload these directly to
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. This position resides in the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL). CSED focuses on transdisciplinary computational science and analytics at scale
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-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory Ability to function well in a fast-paced research environment, set priorities
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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to performance and availability of funding. Letters of Recommendation: Please arrange for three letters of reference when applying to this position. Letters should be sent to postdocrecruitment
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independently and to participate creatively in collaborative teams across the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within
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for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into emerging paradigms
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Requisition Id 16197 Overview: We are accepting applications for a Postdoctoral Research Associate position who will focus on the intersection of material science, materials characterization, and
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Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling