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Campus City Salt Lake City, UT Type of Recruitment External Posting Pay Rate Range $75,000 to $85,000 Close Date 11/03/2026 Priority Review Date (Note - Posting may close at any time) Job Summary The
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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include: Transcripts and diplomas for Bachelor's and Master's degrees CV Copy of Master's thesis. If you have recently submitted your Master's thesis, you can attach a draft of the thesis. Documentation
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upon request. The application must include: Transcripts and diplomas for Bachelor's and Master's degrees CV Copy of Master's thesis. If you have recently submitted your Master's thesis, you can attach a
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representation of the vertical coordinate in numerical ocean models used to simulate ocean currents, temperature, and salinity. These developments will improve the representation of deep-ocean circulation and
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will be combined with theoretical modelling, statistical data analysis, and numerical simulations to identify the governing mechanisms and to determine whether and how proximity to a phase transition
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of investigating the relationships between microstructure, processing routes, and thermomechanical performance. The research will combine physics-based modelling and numerical simulations, including thermomechanical
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parameters (periodicity, thickness, refractive indices) and material properties to maximize the ultrasonic sensitivity of the sensor. 3. Experimental Validation: Comparison of the predicted simulation
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laser beams, in the context of future laser fa-cilities for inertial fusion. It will be carried out within the theory group TIPS (Theory, Plas-ma Interpretation & Simulation), in close collaboration with
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of developing of energy autonomous neural interfaces. The researcher will be in charge of the finite elements simulations to predict and design novel materials and device designs for energy harvesting based