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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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(m/f/d) in the topic: “AI-based processing of CAD models for automated planning of computer-aided manufacturing.” The candidate has the opportunity to pursue a doctoral degree (Ph.D.). Remuneration is
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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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cardiovascular simulations, especially with regard to the estimation of biomarkers for image-based diagnostics. The project is a collaboration between WIAS (Research Group “Numerical Mathematics and Scientific
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methods to quantify the propagation of domain uncertainties in cardiovascular simulations, especially with regard to the estimation of biomarkers for image-based diagnostics. The project is a collaboration
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mass spectrometers and nano/UHPLC systems Experience with state-of-the-art proteomic workflows, including MS acquisition (e.g. DDA, DIA, PRM) and data analysis with dedicated software tools. Computer
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-experimental approaches for causal effect estimation (e.g., regression discontinuity and difference-in-differences) to help us expand our work on the link between shingles vaccination and dementia/ cognition
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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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of how hotspots in human and marine mammal presence are distributed in affected areas. Acoustic ship models will furthermore help to understand and gauge the acoustic footprint of ships in various types