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Field
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for implementing multimodal sensors, particularly in the automotive applications. Knowledge of engineering design, human factor, control, optimization, FEA simulation and experience in sensors will be considered
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individuals with research interests in optimization, evolutionary computation, and machine learning. Initial appointments are for one year. Required Department Minimum Qualifications: Ph.D. or terminal degree
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and optimizing 3D printer hardware and firmware to meet experimental requirements, and integrating these systems into functional laboratory platforms. The successful candidate will work closely with
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the works in technical reports or papers. Knowledge of network placement optimization for various topologies, e.g., Erdos-Renyi random network, scale-free, small world, etc, will be advantageous. key
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scientific writing, towards to OR research community. • Willingness to learn new knowledge on resource allocation and data-driven optimization, particularly in the latest development. • Communication
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processing, mining, chemical or geological engineering, or a PhD in a related field with mining/mineral processing experience. Knowledge of process mineralogy, froth flotation through past research, coursework
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-driven, knowledge-based agentic systems for automated alert triage and investigation in SOC environments, contributing to the AutoSOC platform Build explainable AI models that integrate planning, memory
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). Familiarity with Docker/Singularity for reproducible HPC environments. Experience with CUDA-level optimization or debugging hardware-specific performance differences. Basic knowledge of protein structure
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Innovation Quadrant (IQ) analysis and resource optimization studies to address business challenges. Industry Engagement Collaborate with industry partners to co-create innovative products that enhance
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aims to improve electrodialysis (ED) for REE separation by developing advanced membranes and integrating AI-driven optimization techniques. By combining materials innovation with machine learning