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and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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prevalent is polygraph testing which infers deception through the measurement and analysis of physiological responses (e.g., blood pressure, electrodermal activity). However, despite its widespread
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systems , Quantum Transport in topological phases , Radiation Therapy Simulation , RF Engineering , Scattering amplitudes , simulation-based inference , single- and multicomponent quantum hydrodynamics
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). The goals are to develop new computational methods that allow the scientific inference of explainable attributes to describe human behavior and self-reports as well as to make progress in the computational
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ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
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, perform cutting-edge analytical techniques for causal inference and prediction, and writing papers for both an academic audience and for practitioners (managers and/or policymakers). Desired Qualifications
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will be developed using the OpenFOAM toolkit. A modelling workflow will be created, and then used as the basis for the optimisation, potentially using tools such as Bayesian Optimisation. In addition
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in progress). In exceptional cases, candidates with an outstanding bachelor's degree alone may be considered. Demonstrated competence in causal inference methodologies and econometric identification
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at the intersection of causal learning, inference, and deep learning, leveraging Graph Neural Networks (GNNs) and Large Language Models (LLMs). The successful candidate will explore how GNNs can model causal structures