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is to develop high-fidelity models based on a test-calculation dialogue, seeking the best compromise between the degree of accuracy, the level of complexity, and the effort required to identify
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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]. These systems are characterized by highly nonlinear, anisotropic, and time-dependent responses governed by evolving internal mechanisms and environmental conditions, making their predictive modeling particularly
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during these experiments will be used to calibrate a numerical model of PFAS fate in soils. The predictions from this model will then be compared with PFAS concentration measurements in leachate collected
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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: Computational, Quantitative, and Predictive Modeling of Root Systems. This position emphasizes integration of phenomics and other -omics data into predictive frameworks. Research areas may include: Structural
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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, progression, and treatment outcomes. Skills in applying causal inference, survival analysis, and longitudinal modelling to link clinical and biological data. Expertise in predictive modelling and AI
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breed x system interactions. Including e.g. milk-based parameters according to other WPs, production system specific early prediction models for the control of endoparasites will be developed