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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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modeling. For more details, please view https://www.ntu.edu.sg/cee . We are seeking a Research Fellow to contribute to cutting-edge research in multiscale clay science and geotechnical engineering. The
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Fellow to join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) develops interpretable and autonomous artificial intelligence (AI) systems for biological and biomedical research. Our neuroscience
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-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for microbial bioprocess development; microbial
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related fields. Strong background in Physics-Informed Machine Learning (PIML), scientific machine learning, or data-driven modeling for engineering systems. Expertise in numerical simulation of multi
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vivo (e.g., brain organoids) and in vivo (e.g., mice) experimental models. Our Group collaborates with colleagues based in two international consortia: CHARGE and ENIGMA. Our research takes place in both
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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, Lingnan University is transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https
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leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/ . Applications are now invited for
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group (https://sbnb.irbbarcelona.org ), led by Dr. Patrick Aloy, to work on Systems Medicine approaches in the context of the CLARITY European collaborative project. Viral infections, together with human