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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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are initially limited to three years. The Microbiome Dynamics (MBD) department contributes to the development of personalized microbiome-based strategies to aid in the detection, monitoring, treatment, and
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 23 days ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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, ocean, marine ecosystem, and impact models of different complexity and will include both traditional and new ocean modelling approaches with the final objective of delivering: (i) coordinated and
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to the development of personalized microbiome-based strategies to aid in the detection, monitoring, treatment, and prevention of human diseases (e.g., Li et al., Nature Metabolism, 2024; Ni et al., Cell Metabolism
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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reliability of R-Mode, particularly under varying environmental conditions. Key objectives include understanding the physical processes that affect R-Mode signal propagation, quantifying the variability
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/ . By submitting your application, you confirm to have read and understood the data protection information provided by TUM. Find out more about us at www.tum.de. Application Materials Required: Submit
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utilizes a flexible and advanced detector setup to investigate nuclear structure and astrophysical processes through the detection of decay radiation from exotic nuclei. Experiments are currently being
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in scope? Critically assessing the added value of 3D-based generative modelling compared to 2D-based approaches Tailored reward functions towards specific design objectives Dynamically incorporating