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fields. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic
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methodologies. Proven proficiency in thermodynamic modeling using FactSage, CALPHAD, and complementary simulation platforms like Aspen Plus. In-depth knowledge of electron microscopy, spectroscopy, and structural
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indirect costs of urbanization. Calibrate this model using real data from specific case studies. Simulate urbanization costs based on various climatic, morphological, and social scenarios. Formulate
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calculations to model and optimize heterogeneous catalysts. Conduct simulations to evaluate catalytic performance and reaction mechanisms. Analyze and interpret computational data to understand the interactions
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trials based on experimental design. Contribute to the numerical modeling of beneficiation circuits (e.g., using USIMPAC or similar software). Analyze, interpret, and report the outcomes of simulations and
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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review publications. Develop and implement simulation models to predict yields from secondary data sources and established on-farm trials in Africa. Conduct spatial analysis to predict yield at scales
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CFD methods DFT and Molecular modelling linked to CCUS Strong analytical and problem-solving skills, with an ability to interpret and analyze simulation and/or experimental results. Ability to work in
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, Chemical Engineering, Process engineering or a related field. Requirement Strong knowledge in process engineering Proficiency in using simulation software for process modeling (e.g., ASPEN, SuperPro Designer
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interfaces, and condensation surfaces under varied operating scenarios. Develop and refine performance simulation models and predictive tools to support system optimization and deployment strategies. Prepare