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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
simulation tool for modelling hydrogen isotope transport in materials. The role is central to enhancing the scientific reliability and benchmarking capacity of FESTIM in support of fusion fuel cycle modelling
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the Laboratory's collective success. The Division of Chemical & Biological Sciences is seeking a Postdoctoral Research Associate with a strong background in quantum chemistry and molecular modeling. The selected
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model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Experimental data analysis in hadronic physics Superconducting electronics and sensors Detector simulations
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A postdoc position is immediately available at the Advanced Photon Source of Argonne National Laboratory. The postdoctoral appointee will develop ultrafast microscale photonics and MEMS
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hardware modeling; discrete event simulation for quantum networking; novel auto-differentiation techniques for discretely random processes and circuits; and more. This position is expected to lead to the
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the project goals. The successful candidate will be working closely with the project PI’s to generate and test hypotheses, develop and run model simulations, and report findings through presentations
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to neurovirulence. Preferred Qualifications: PhD or Postdoc experience in Flavivirus research. Experience in BSL3 with neuropathogenic viruses and ABSL3 murine models. Two (2)- Five (5) years of molecular biology
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to neurovirulence. Preferred Qualifications: PhD or Postdoc experience in Flavivirus research. Experience in BSL3 with neuropathogenic viruses and ABSL3 murine models. Two (2)- Five (5) years of molecular biology
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well as the residency programs and clinical training of Humanitas University, an international Medical School that stands out for its real-world simulation-based approach to medical training. Humanitas Research is made
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning