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experience in the fields of computational mechanics and computational geomechanics. Preferred Qualifications: Experience in the following areas is preferred: Experience in modeling geomechanical systems
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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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Reality to elicit users’ preferences for innovative transport systems. Applicants with a background in behavioral analysis and mathematical modelling are encouraged to apply. Terms of employment include
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of existing and emerging networks and communication systems, with a possible starting date in January 2025 (or later). The group’s research builds upon the areas of system, network, information, and computer
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modeling and characterization for communication and sensing in emerging spectrum for 6G and beyond, with a focus on the FR3, THz and optical frequency bands. This research will be conducted under the joint
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the frontiers of developmental biology and disease modeling. The laboratory integrates stem-cell biology, fluorescence imaging, bioinformatics, and advanced nano- and micro-engineering to decode organogenesis and
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, Neuroscience, or a related field. A strong background in functional neuroimaging with experience in decoding and/or encoding models is required. Candidates with experience with recurrent neural networks will be
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, climate, and human health. Examples of current active projects include: Developing optimization models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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, climate, and human health. Examples of current active projects include: Developing optimization models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems