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Field
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control and optimization strategies, run high-performance numerical experiments, analyze data, and communicate your findings through journal publications and conference presentations. The ideal candidate
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contribute to the following activities: - Design and optimization of functional formulations containing luminescent nanoparticles - Study of colloidal stability, rheological behavior, and dispersion quality
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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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liquids and/or deep eutectic solvents for API synthesis; optimization of the process conditions with emphasis on reduction in use of organic solvents; controlling the conditions of the process and its
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-transport-layer engineering, dark-current suppression, metasurface integration, optical-field manipulation, and low-noise detector characterization. The successful candidate will translate optimized CQD
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developing, optimizing, or troubleshooting complex experimental or computational workflows. Experience working in a collaborative or multidisciplinary research setting. Experience mentoring or training
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focused on the use of enabling technology, such as flow, to control complex chemical processes. With projects in supramolecular synthesis, organic materials, industry processes, automated optimization, and
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be responsible for conducting and optimizing chemical synthesis activities targeting specific proteins. Mandatory requirements - PhD in a field related to organic chemistry or biological chemistry
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-source modeling and collaborative research. The successful applicant will play a central role in developing and applying Tools for Energy Model Optimization and Analysis (Temoa), a widely used open-source
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though optimal sizing of chargers and mobile battery storage systems based on the demand and through energy management strategies under traction and AC grid constraints. Contribute to two pilots