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-package of the ChiExCo program, which aims to develop a reliable computational protocol to predict, for organic chromophores, both chirality quantifying factors (gabs and glum) resulting from excitonic
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work on research projects employing latent variable modeling and risk prediction methods to better understand substance use related morbidity and mortality outcomes (e.g., overdose, hospitalization
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for predictive modeling scenarios, causal modeling is also within the scope of the position. The position is embedded in the ten-year gravitation grant Stress in Action, funded through NWO (Dutch National Science
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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
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for the next 2.5 years at the interlink of prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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the following research areas providing a template for relevant directions: - Embodied Intelligence for Soft Robotic Systems - Foundational Models for Adaptive Soft Robots - Real-Time Adaptive and Stiffness-Aware
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interventions can promote cognitive abilities in aging. The main task is to develop methods for predicting health outcomes using dynamic and adaptive modeling whilst addressing computational challenges
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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from