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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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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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to reason about software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new
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methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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populations from New Guinea using museomic data Assembling high-quality reference genomes and generating whole-genome resequencing data of avian skins Inference of evolutionary history using Ancestral
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position is funded by multiple NIH projects, e.g., https://tinyurl.co m/ysxhmujvThe overall goal is to : (1) develop inference and dynamic prediction models using a wide variety of data, including clinical
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. Bonus lectures can be picked by the students depending on their interests and project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI
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eye tracking, we will study how people coordinate and interact within these settings. The project is carried out in collaboration with partners at RWTH Aachen University and the University of Rennes