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others. A typical observation model (giving the likelihood of the data p(X|A)) states that the different materials contribute linearly in each pixel: X = SA + E where X gathers all the L-dimensional pixels
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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Institute of Microbiology is seeking a postdoctoral associate to participate in our studies of reproductive biology in the C. elegans model system. Specifically, the candidate will contribute to our
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thermal management systems for electronic devices. Ac-tivities include a critical literature review and conceptual develop-ment of systems based on heat pipes and phase change materials (PCM). This will be
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synthesis, material science, theory and modeling, device physics, nanotechnology, biotechnology, and system design. Our activities span the range from basic research to commercialization, the latter carried
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in the project “Developing new approaches and structural materials to rebuild damaged Ukrainian infrastructure with environmental sustainability considerations“ funded by the Polish National Science
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on integrating material and manufacturing behavior with mechanical and thermal performance models, manufacturability considerations, and sustainability metrics within a system-of-systems perspective. The work will
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Assistant Professor faculty position in the area of computational materials science with interests that may include, but are not limited to, quantum materials, multiscale modeling, and data-driven design of
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. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and
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models using induced pluripotent stem cell (iPSC)-derived kidney organoids. A key aspect of this work will involve integrating bioprinting and organ-on-chip technologies to create mature and