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in at least one of the following: optimization, PDEs, optimal control, numerical analysis/scientific computing, or machine learning for differential equations and control. Evidence of research
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: Perform high-fidelity thermal and mechanical numerical simulations for metal additive manufacturing. Develop and implement PIML models for analysis and optimization of metal additive manufacturing
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do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries to improve sensor performance
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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effects of urban greenery and develop a modelling platform to assess and optimize the cooling benefits of various types and configurations of urban greenery in Singapore Key Responsibilities • Conduct multi
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simulated wave conditions. Perform numerical study of the model test. Analyze and interpret data to make informed recommendations for costal protection system design and optimization Writing and publication
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, with focus on CO, NOₓ, N₂O, and unburned NH₃. Generate modelling insights and tools to support the design and optimization of low-emission, ammonia-fueled propulsion technologies. Job Requirements: PhD
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objectives to be achieved: The work plan includes numerical modeling of the injection modeling process using commercial software, and the integration of these programs with AI-based optimization methodologies
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outline their research plans to optimally extract cosmological information using one or multiple of these datasets. Priority will be given to candidates who can connect their science to SPHEREx, Simons
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theory of the finite element method is an advantage. Competence in the theory of numerical optimization is an advantage. Experience from high-performance computing is an advantage. Applicants must be able