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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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. The Built Environment and Sustainable Technologies (BEST) Research Institute is seeking a highly motivated Research Fellow in Automation and Control to support the advancement and scaling of biomanufacturing
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FGFs and other lightning-generated high-energy radiation, then using radio and optical predictions from this model to compare against ALOFT observations to ultimately solve the FGF puzzle while
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of their unaccreted "runners" (escaping projectile planets) and lesser remnants and debris. The goal is to make specific giant impact formation hypotheses more predictive and testable, through this end-to-end approach
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on advancing Predictive, Preventive, Personalized, and Participatory (P4) approaches in health and medicine. Within the IRAP framework, the project’s scientific goal is to discover and validate novel therapeutic
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hypotheses more predictive and testable, through this end-to-end approach, with a particular emphasis on the Earth, Moon, Venus, Mercury, and Mars, but also terrestrial planets around e.g. a red dwarf star
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to identify, quantify and compare these precursors using controlled laboratory experiments on granular systems combined with advanced optical measurements, with the objective of improving failure predictability
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 15 days ago
validation of human-based in vitro and computational methods submitted to FDA for regulatory use. The developed models will be used to predict various toxicity endpoints and drug exposure to help provide
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of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper