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Characterization Data 2. D2.3 – Durability data • Work Package (WP) 3: Modelling and Simulations Deliverables: 1. D3.1- CFD Report 2. D3.2- Thermo Report 3. D3.3- Optimized Model Report Where to apply Website https
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architectures (including gap-waveguide technology) to maximize isolation, efficiency, and compactness. The successful candidate will engage in antenna design, electromagnetic modeling, array integration, and
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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Description The Industrial Informatics group at FEE/RICE is focused on the application of advanced optimization techniques to: i) design of new electro-mechanical devices using numerical simulations, ii) system
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California State University, Northridge | Northridge, California | United States | about 18 hours ago
*NOTE: To view the full position description, including all of the required qualifications, copy and paste this link into your browser: https://mycsun.box.com/s/97zib9u6ny7c5hrv8dxxv6b8nfkp5i5x
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socio-economic analysis, equipping them with advanced skills in reservoir modelling, machine learning, advanced oxidation processes, and microbial enhanced recovery. Candidates will also develop intuitive
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Modeling and Simulation of Energy Storage Units interacting with the electric grid - 2026_CDR_DENG_1
storage system (understood as a whole, considering the cells, the BMS, the PCS, and the auxiliary systems), as well as their modeling in mathematical optimization tools and, finally, in dynamic simulation
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combined with other geophysical and petrophysical data. In addition, coupled thermal-hydraulic-mechanical numerical simulations will be carried out. We are looking for a scientist for electromagnetic
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, build, and experimentally validate an inverter for a 100 kW, 20,000 rpm motor. As a PhD student, you will contribute to the simulation and modeling of the inverter and its integration with the electric
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting