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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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, 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
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge