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, including physics-informed neural networks, neural operators, hybrid physics-ML approaches, and emerging foundation-model paradigms for scientific data. Scientific machine learning is increasingly important
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on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models
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Position 1 - Hybrid Traffic Flow Modelling This PhD focuses on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed
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of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training
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challenging, especially in dunes with complex topography. Your job In this 2-year postdoc position, you will use numerical modelling and machine learning techniques to increase the accuracy of coastal dune
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environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited
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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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novel methodology for the analysis of single-cell (multi)-omics data by incorporating existing biological knowledge into machine learning models. You will join a collaborative and internationally-oriented