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Organization U.S. Department of Defense (DOD) Reference Code ERDC-CHL-2025-0006 How to Apply Click on Apply now to start your application. Application Deadline 5/30/2025 3:00:00 PM Eastern Time Zone Description The U.S. Army Engineer Research and Development Center's Coastal & Hydraulics...
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environmental and social impacts; 2) enhance the integrity and efficiency of the FPL’s research efforts through the development, evaluation, and application of modern statistical methods; and 3) provide life
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wearables. Experience in experimental design and analytical characterization is necessary. Working knowledge of data analysis and analytical methods including spectroscopy and microscopy is preferred. Primary
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formal GIS course • Provide a sample map from their portfolio upon request A complete application consists of: Zintellect Profile Educational and Employment History Essay Questions (goals, experiences, and
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of the Adaptive Hydraulics (AdH) framework, as well as advancing high-fidelity computational fluid dynamics (CFD) methods for free-surface and multiphase flow simulations. Activities may include improving and
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. Qualifications The qualified candidate will have completed a PhD from an accredited institution in bioengineering, biomedical engineering, immunology, cell / molecular biology, physiology, or a related field
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developing new approach methodologies (NAMs) for in vitro skin sensitization test methods. Why should I apply? Under the guidance of a mentor, you will gain hands-on experience to complement your education and
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reduction techniques and numerical methods for subsurface and surface flow processes. You will gain experience with forward hydrodynamic models developed in the Coastal Hydraulics Laboratory and contribute
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and prepare for readiness during operational tasks. The team is further exploring novel AI method developments, including applied mathematical and machine learning solutions for real-time use. Why
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for the U.S. energy future. Resource estimation methods to determine tonnage and grade of these unconventional feedstocks is still evolving, and requires refined approaches that leverage probabilistic modeling