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regarding the reliability and translational value of in vitro models for predicting carotenoid bioaccessibility across diverse food matrices. The findings will have important implications for nutrition
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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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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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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parameters using experimental muscle and neural recordings Explore motor control policies that replicate observed behaviours Test simulation predictions against muscle ablation experiments Investigate how
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17 Mar 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Modelling tools Geosciences » Geology Geosciences » Hydrology Researcher
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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California State University, Northridge | Northridge, California | United States | about 7 hours ago
plus. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties