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Field
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to the Singapore Centre for 3D Printing (e.g. competitions, conferences and hosting visits) Requirements: Ph.D. in the area of Industrial, Mechanical, and Materials Engineering Engineering Design and Optimization
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at peer-reviewed venues and conferences. Example project areas Performance analysis and optimization of end-to-end scientific workflows, including those originating at DOE facilities. Operating persistent
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detection and prevention strategies Disparities research and risk modeling to optimize equitable deployment of new cancer screening modalities The fellow will have opportunities to lead analyses within
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developing optimized modules in C#/C++ within Unity and/or Unreal Engine Experience with database management systems For network engineer role: Experience in computer networking, multiplayer architecture, and
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applications. To verify the potential of thousands of materials optimal for clean energy technologies by synthesizing, analyzing and optimizing the characteristics of the materials experimentally will demand
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the award of 1 (one) Research Grant within the scope of the I&D project Mod3DConstruction: 3D printing in the prefabrication of customizable thermally optimized modular façade panels, reference no. 14927, co
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to aquaculture health and/or therapeutic delivery. Develop, optimize, and evaluate delivery platforms (e.g., nanoparticles, encapsulation, oral delivery systems, or biofilm-based approaches). Design and perform in
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Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR/clinical trial data Additional Qualifications: With this appointment, you are
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record of or exceptional promise for research. The fellow will work with Prof. Sam Petti on developing methods for modeling, optimizing, and interpreting biological fitness landscapes. Successful
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developing an efficient storage solution for AI applications deployed at HPC centers. In detail, the work will focus on the development of a storage solution that optimizes the persistence of checkpoints of AI