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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending
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, creating predictive models for failure control. Validation & Experimental Collaboration: Compare simulations with experiments, collaborate on proof-of-concept testing, and refine models based on results
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microfluidic analogs of phloem sieve plates and other plant hydraulic elements. Conduct controlled flow-pressure experiments to evaluate aspects of the theoretical predictions and quantify resistance mechanisms
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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IF or CORE A*/A conference paper. Robust machine control assumes modeling of robot-environment interactions. An example may include an outdoor autonomous ground robot that needs to be aware of its
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sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with the microgrid model and
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is searching for a Control Engineer for developing health-aware model predictive control (MPC) for fuel cell hybrid electric vehicles (FCHEVs). Fuel Cell HEVs provide a long-term solution to