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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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, case-control studies, cohort studies, structural equation modeling, geospatial modeling, missing data, population-level risk prediction, and measurement errors Community-based research, health
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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defects, are currently a major limiting factor for metal printing. In nanomedicine, various nanoparticles are used for controlled drug delivery and therapies, and laser-excited nanobubble-inducing shockwave
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systems or smart buildings, such as regression, classification, time series analysis, or basic predictive modelling. Experience with data handling, including data cleaning, transformation, exploratory
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or intervention strategies are lacking, urging the need for new perspectives on pathogen control. Within this project these perspectives will be explored. To predict correlates of disease against these complex
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digital twins be used to provide on-line predictions as to the future expected evolution of these critical properties as the basis for safe reinforcement learning (RL) for on-line optimal control”. In
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–2023), the model demonstrated good predictive performance for daily and weekly dengue cases based on two years of sentinel hospital data. As part of the ANRS SEA-ROADS programme (2024–2027), coordinated
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interactions. This involves (i) developing predictive machine learning models that forecast user actions and remote system responses across audio, video and haptic modalities, and (ii) jointly orchestrating
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unsupervised learning Distributed / decentralised command and control: synchronisation, coordination, adaptation, for example using multi-agent systems Decision support under uncertainty Modelling and simulation