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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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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 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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E13 Job Description The successful candidate will join an interdisciplinary team focused on applying bioinformatics to personalized oncology. The primary objective is to unravel molecular pathways
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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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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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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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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