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supports applied food processing research projects with strong industry relevance, focusing on product development, process optimization, and food characterization. Key Responsibilities Lead and manage
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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | about 14 hours ago
of new in vitro methods to detect and measure venom components, and to optimizing antivenom potency assessment Acquire experience in venomics and ecological effects that cause variability in species
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at Cambridge University and in Germany and the US, we will work from the scale of the single gene to field evaluation to define a framework for rational optimization of plant-mycorrhizal symbioses for more
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team (external). Assist with the development and optimization of laboratory and analytical workflows. · Contribute to grant applications, manuscripts, reports, and presentations, with strong
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the development and optimization of laboratory and analytical workflows. · Contribute to grant applications, manuscripts, reports, and presentations, with strong scientific writing skills and attention
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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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support systems, protective clothing, precision airdrop systems, and ballistic, chemical and laser-protection systems, and human performance optimization. About ORISE This program, administered by Oak Ridge
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will work across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and
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. Competence in the 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
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desirable. Experience with AI, machine learning, optimization, spatial analysis, scenario modeling, or decision-support methods is strongly preferred, especially when applied in transparent, interpretable