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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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for acoustic metamaterials/metasurfaces; use simulation and data-driven optimization; contribute to listening tests and VR-based soundscape evaluation; build or test prototypes; publish results; and contribute
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications, reports and seminars The selected candidate will be part of the Foundations Cluster of CIDSAI and
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well as to investigate protein–ligand interaction properties; performing protein crystallization and crystal optimization using the robotic facility available in the Structural Biology Laboratory; providing samples and
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method development and large-scale research facilities. Essential Duties and Responsibilities Develop workflows to improve models and maps to optimize likelihood for ligand placement Develop AI/ML methods
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-throughput data, identify differentially expressed genes, explore protein interaction networks, and perform pathway and functional enrichment analyses. Responsibilities also include developing and optimizing
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developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python
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polymerization techniques; 2) development and optimization of protocols for the preparation of nanoparticles; 3) evaluation of the unique properties and functionalities of the developed biomaterials. This position
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the nexus of AI, operations research and decision making and their applications to workforce policy and reskilling. The project builds on ongoing work using optimization-based models to predict future skill
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, perform, troubleshoot, and interpret experiments. Ability to rapidly learn, optimize, and implement new or advanced assays as required by the evolving research direction. Experience leading or substantially