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Field
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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optimization, and covalent drug design. Collaborating with researchers and engineers at the Monash Institute of Pharmaceutical Sciences and QDX Technologies to implement scalable AI–quantum hybrid platforms
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culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach others every day. Visit our website to learn more about the University at Buffalo. University at Buffalo
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and responsibilities. The applicant is expected to have expertise in the design and optimization of electrical and mechanical components, including actuator selection and control, CAD workflows
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and chemoproteomics studies Help develop and apply biochemical, biophysical, and metabolic stability assays Screen compounds; analyze and interpret data Apply data to the iterative design of optimized
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optimizing simulation tools such as CalPhad to support experimental findings. Conducting in-depth metallographic analysis and establishing correlations between mechanical properties and microstructural
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of expression systems Metabolic flux analysis for pathway characterization and optimization Development and operation of continuous fermentation processes under strict anaerobic conditions Strategic contribution
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Department Name RADIATION ONCOLOGY Location Code RADIATION ONCOLOGY (06720A) Is this split and/or fully grant funded? Yes Duties and Responsibilities Dosimetry techniques optimization: Accurate dose