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Computational Neuroscience ● Mathematical/Theoretical Foundations of Artificial Intelligence Applications should hold a PhD in Computer Science, Electrical and Computer Engineering, Applied Mathematics
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Johns Hopkins University, Department of Physics & Astronomy - Physics of Learning fellowship ID: Johns Hopkins University -Department of Physics & Astronomy - Physics of Learning -POSTDOC [#29358
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achievements, 2) how you see your past achievements fit with the WEB CHILD project (empirically/theoretically/methodologically) 3) what your interests are in terms of working on the WEB CHILD project, 4) what
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Projects: Identifies important nuclear-physics uncertainties for stellar models and, with local experimental and theoretical faculty and researchers, designs future studies to reduce those uncertainties. Why
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Computational Neuroscience ● Mathematical/Theoretical Foundations of Artificial Intelligence Applications should hold a PhD in Computer Science, Electrical and Computer Engineering, Applied Mathematics
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Engineering of Additive Manufactured Metals—MicroAM’. The MicroAM project aims to develop (i) a laboratory-based X-ray microscope capable of mapping local residual stresses, (ii) a multi-scale AM process
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to ion beams with well-controlled energies and incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes
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A position exists for a Post Doctoral Research Associate in Department of Applied Mathematics and Theoretical Physics to work on the theory and implementation of algorithms and protocols on quantum
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with pending graduation will be considered. Experience with theoretical approaches in thermodynamics and numerical modelling. Preferred Qualifications: Physical Requirements and/or Unusual Work Hours
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theoretical questions about eco-evolutionary dynamics and has a passion for science. The postdoctoral position is part of a DFG project and centres around addressing questions on eco-evolutionary dynamics