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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 11 days ago
-tolerance to the insulin producing beta cells, and 2) Development of immunotherapies for T1D. The work makes use of several genetically manipulated mouse models of T1D, as well as humanized mouse models, and
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quantum many-body theory with a focus on quantum impurity models (particularly Kondo model). Strong computational skills (with Python or Julia or C++ or Matlab or equivalent) and using numerical techniques
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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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Astrophysics, Space Physics, Aerospace Engineering, Applied Physics, or a closely related field. Previous experience with numerical modelling and simulation relevant to space systems of astrophysics and a
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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
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04:59 AM UnitedKingdomTime) Position Description: Apply Position Description One or possibly two postdoctoral positions to work on the theory of generative AI—particularly diffusion models and their
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excellence of NTU and attracting interest of more industrial collaborators. Key Responsibilities: Develop a new and robust numerical model with lattice Boltzmann method to simulate flow and heat transfer with
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, vegetation, and water table position on energy transmission. Co-develop and apply process-based numerical models of dune morphological change informed by collected datasets. Coordinate field campaigns
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. For the Working Group “Ecosystems in Transition” in RD1 at PIK, we are offering a PhD position (m/f/d) (Position number: 10-2026 PhD ARTECO) in the field of ecological modelling of complex arctic landscapes
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professors and almost 50 senior researchers, as well as around 50 doctoral students. The AM division conducts broad research in applied mathematics, with an emphasis on numerical analysis for partial