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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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: active learning (uncertain cases first), smart sampling, confidence thresholds, gradations (auto-label/review/manual), measurement and decision logic for throughput vs. quality. Proficiency in programming
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Learning, especially in spatiotemporal modelling, environmental data analysis, or multimodal learning, Practical experience in applying Machine Learning, ideally including deep learning, foundation models
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developing a digital twin, employing machine learning and numerical computations of atomistic processes. At IKZ, a kinetic Monte Carlo tool has been developed in the programming language julia. This allows a
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
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funded German research initiative. Project Description: Carbon black is an indispensable component of numerous everyday products – from car tires and seals to paints and plastics. However, its production