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Field
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this knowledge gap and establish improved GHG models accounting for soil invertebrates. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental
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annotation, image recognition, data extraction); Development and maintenance of statistical software tools for causal inference and open science applications. Your qualifications profile Enrolment in a
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learning models Implementation of deep learning Improvement of models, e.g. in terms of efficiency, training performance or inference behavior We promise you excellent contacts in the relevant industrial and
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Bayesian belief networks; Experience in scenario development approaches, e.g. SSPs; Experience in the application of R-based analytical tools for qualitative or semi-quantitative modelling, incl. RQDA
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. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental parameter measures and DNA data. Your Tasks Participation in fieldwork in Germany
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Review, update, and consolidate methodologies, including Bayesian methodologies, in the context of material balance evaluation Your Profile: PhD in applied mathematics, computer science, physics, or in
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Correct), Bayesian statistics, systems theory and artificial intelligence are to be used as a basis in order to explore and implement a continuous calculation chain starting from observable and controllable
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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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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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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