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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 2 hours ago
-reported tolerability endpoints, or other endpoints of interest. Statistical approaches under evaluation could include joint models for longitudinal and time-to-event data, mixed-effects models for repeated
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application consists of: An application Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal
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statistics package, including data management, specification of different analytical models, randomisation, statistical matching, power calculations, panel datasets, multiple comparison adjustments, missing
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, computational modelling and related research topics. Develop, implement and validate computational simulation models for impact and injury prediction using appropriate numerical methods and software tools. Design
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, advanced manufacturing, refining, and mining — and the pressure that growth places on shared energy, water, land, and community systems. Purpose Build and apply quantitative and economic models to assess
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SolidWorks. • Mathematical modeling and simulation analysis of pneumatic actuator components (including formula derivation and parameter optimization). • Conducting experiments to characterize
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. The research will involve developing analytical models, performing large-scale numerical simulations of open quantum systems, investigating decoherence and noise processes, and exploring new architectures and
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focused on the development and application of recycled and waste-derived materials for advanced engineering systems. The role will emphasise computational modelling, simulation, and data-driven approaches
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projects Demonstratable experience of applying agentic AI and sequence-to-signal deep learning modelling Demonstratable experience generating Nextflow Pipelines Desirable criteria Demonstratable experience
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experience in atomistic simulations is preferred. • Development or working experience of generative models in scientific applications (e.g., diffusion/flow model) and working experience with agents