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. This research aims to overcome these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling
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/articles/s41467-024-49299-6 https://www.nature.com/articles/s41590-022-01208-z As a PhD candidate in our group, you will study and model the biomechanical chain of events following (blast) impacts
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model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate will also design and develop new software and prototype models
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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate
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, such as semi-Markov models linked to disease transmission models. The framework builds on existing models but these must be adapted to account for differences in epidemiology and disease burden between
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patient stratification and combination therapy choices. You’ll be part of a tightly integrated team that uses computational models generate hypotheses and, with the help of partner labs, validate them in
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to apply Website https://www.academictransfer.com/en/jobs/357715/phd-high-resolution-modelling-o… Requirements Specific Requirements We are looking for a motivated candidate to develop and apply high
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verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based automated reasoning tools such as ESBMC (https://github.com/esbmc/esbmc ) to address safety and reliability
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, the weaknesses that have been evoked in the conclusion of most work is the unverified or lack of adaptability of models developed in specific case studies to new environment. Objectives: The PhD research will
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27 Jan 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Computer science » Modelling tools Computer science » Programming Engineering » Civil