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of the additives in phosphate ore processing. Study the interaction mechanisms between additives and mineral surfaces using surface chemistry and interfacial analysis techniques. Contribute to the optimization
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development in powder bed electron beam additive manufacturing (EB-PBF), with a focus on AI-driven process planning, heat simulation, and process optimization. The work includes developing physics-based AI
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about equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required Related Experience 1 yr Required License/Registration/Certification Licensed Pharmacist Physical Requirements
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of Medicine and Department of Pediatrics Department: Medicine Posting Link: https://www.ubjobs.buffalo.edu/postings/59551 Job Type: Varies Posting Detail Information Position Summary The University at Buffalo
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physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support
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campaign gift announcements, integrating resources from across the university. Digital strategy, user experience, and optimization (25%) Provide strategic leadership for the campaignâ™s digital ecosystem
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application workflows across CPU/GPU clusters, parallel storage, and hybrid cloud platforms. Responsibilities include configuring and optimizing large-scale Linux environments, implementing job scheduling and
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previous work to channel flows, both with and without wall roughness. The research will focus on, firstly, optimal synchronisation of minimal unit turbulence (MUT), leveraging the simplified dynamics in MUT
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at the Massachusetts Green High Performance Computing Center (MGHPCC). You will help with optimization of code and utilization of scheduler features to maximize throughput of jobs. In addition, you will work with
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Optimization and Sustainability Cluster roadmap, focusing on: Process modeling, simulation, and optimization for chemical, energy, and environmental systems using Aspen Plus, Aspen HYSYS, and other advanced