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holder will lead research activities centred on reinforcement learning, optimal control, and simulation-based policy development, using robotic and biomechanical simulation environments to design and
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uptake, stress tolerance, and species interactions. A case in point is optimal partitioning theory (OPT), a dominant paradigm of plant resource allocation that describes the ability of plants to adjust
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genomics teams at The Ohio State University, and USDA Agricultural Research Service (ARS). The research will evaluate multiple strawberry genotypes grown under controlled environments and optimize
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week/ 50 %), initially limited to two years with possibility of continued employment. The successful candidate will be involved in operating, maintaining, and optimizing high-end imaging and spatial
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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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of their organelle dynamics, metabolic regulation, and potential for biotechnological innovation. Key Responsibilities Engineer and optimize synthetic pathways in yeasts. Modify cellular redox and cofactor systems
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-oriented problems involving optimization, artificial intelligence, workflow redesign, and stochastic assembly line balancing. The position will involve close collaboration with industry partners
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research projects, including formulating hypotheses, designing experiments, and optimizing protocols. Competence in acquiring, handling, and analyzing large and complex datasets, ensuring data quality
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and optimize sulfur cathode materials and related host structures Perform physical, chemical, and structural characterization of battery material Fabricate and evaluate lithium–sulfur cells in coin-cell
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor