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skills. Preferred Qualifications: Prior research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization
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fuels (hydrogen, methanol, ammonia), simulation tools for marine engines and/or fires due to fuel leakages, data analysis methods and their applications for ships, sufficient understanding of appropriate
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 4 days ago
of spectroelectrochemical methods and their application to characterize Tc complexes. The postdoctoral position is part of the project “Retention and solubility of dose-relevant radionuclides under the reducing near-field
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-Performance Computing for Exascale" contributes to the design and development of numerical methods and software components that will equip future European Exascale and post-Exascale machines. This program is
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). Experience in interdisciplinary team-based research. Preferred Qualifications: Prior research experience in computational biology, applied mathematics, or quantitative biology. Familiarity with numerical
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of seismic methods and numerical simulations, Good PC and programming skills (e.g., with Python, MATLAB), Experience with measurement techniques and field measurements using sensor technology (ideally using
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts
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, foreseen in the application: 1-Development of the SUPERB framework 2- Definition of building classes and numerical models for physical vulnerability assessment 4-Definition of baseline data reflecting
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in