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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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://www.yantlab.net/ ) Using large-scale graph-based pangenomics and forward evolutionary simulations, the student will develop predictive models of polyploid genome evolution across contrasting timescales. The student
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. You would be welcomed in the the Yant Lab (https://www.yantlab.net/ ) Using large-scale graph-based pangenomics and forward evolutionary simulations, the student will develop predictive models
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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify
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to influence chemical reactions Chemical system simulation or thermodynamic/speciation modeling (e.g., HSC Chemistry, FactSage, or similar) Characterization of materials using techniques such as ICP-OES/ICP-MS
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on a microeconomic theoretical foundation, applied econometrics and economic modelling to analyse both uptake and consequences of uptake. Furthermore, the project will use a variety of data including
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and economic modelling to analyse both uptake and consequences of uptake. Furthermore, the project will use a variety of data including farm-level panel data and survey data. Preparations of existing
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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. 10 PhD vacancies are available in six universities. For more information, regularly check the individual vacancy pages of the universities listed on the project website [https://heritour.eu/ ]. About