44 combinatorial-optimization Fellowship positions at Nanyang Technological University in Singapore
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distribution network optimization and flexibility planning using distributed energy resources. The successful candidate will develop and implement a flexibility planning tool for electrical distribution grids
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of the School of Electrical and Electronic Engineering in the areas of control and optimization, sensing and information processing, machine learning, cyber physical systems, robotics and automation, power
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learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization for complex design in material and engineering. Key Responsibilities: Collect relevant
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Conversion of Biomass” is looking for a Postdoctoral Researcher in Development of automated self-optimizing small-scale flow processes. The primary objectives are generation and optimization of automated flow
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: The research fellow will Utilize recently developed mathematical tools from algebraic topology, combinatorial topology, computational topology, and differential geometry (e.g., Betti numbers, Hodge
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discipline. Demonstrated expertise in algebraic and/or combinatorial coding theory. Proven track record of scientific research productivity. Ability to work effectively in an international research environment
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Conversion of Biomass” is looking for a Postdoctoral Researcher in Design of Flow Chemistry Protocols. Key Responsibilities: Validation and optimization of existing batch procedures with automation in mind
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optimization for monoclonal antibody discovery. Conduct mouse monoclonal antibody generation using hybridoma technology. Carry out antibody characterization (binding affinity, specificity, epitope mapping
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of their mechanisms, and hit-to-lead optimization. Key Responsibilities Perform recombineering in non-tuberculous mycobacteria. Conduct cell-based assays, microbiological studies, and genetic investigations. Lead mode
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representation with deep-learnt visual features, toward machine uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a