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by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 15 days ago
-Neptunes and gas giants (3 years). Developing next-generation techniques for atmospheric characterization through retrievals with machine learning, including techniques such as simulation-based inference
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 16 days ago
, i.e. the green transition, with the ultimate goal of reaching climate neutrality by 2050. By leveraging characterisation, multiscale modelling, machine learning, and numerical optimisation, the project
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fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic dissection of multimodal
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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engage with it in the future. Qualification Required Master’s degree (or equivalent) in Computer Sciences or a related field by the beginning of the project Experience in machine learning Ability
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, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will: Develop and apply machine
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation