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analysis (TEA). The successful candidate will: Develop innovative strategies for transforming industrial byproducts into high-performance porous adsorbent materials. Design and optimize structuring and
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process intensification, sustainability, and advanced process control, aiming to develop AI-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex
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focused on Artificial Intelligence (AI)-driven retrosynthesis and reaction prediction. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic
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will focus on the formulation and synthesis of advanced synthetic polymers with optimized architectures and tailored functionalities for industrial applications, such as wastewater treatment, fertilizer
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formulations, including additives, collectors, defoamers, or related specialty chemicals, is considered an asset. Familiarity with green chemistry principles, sustainable synthesis, and process optimization is
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sustainable and optimized processes for vanadium extraction and purification, with the goal of enabling large-scale redox flow battery deployment for energy storage. Scope of Work The successful candidate will
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computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. The ideal candidate should have a strong background in artificial intelligence and
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-products. Monitor and control the migration of radionuclides and heavy metals throughout industrial processes. Optimization of Industrial Processes: Utilize nuclear techniques to enhance the efficiency and
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Postdoctoral Researcher in Mineral Processing, Battery Materials, and Sustainable Process Engineering. The researcher will join a multidisciplinary research program aimed at optimizing natural graphite for Li
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart