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Europe. In the Monitoring & AI department, you will be involved in the development and implementation of AI and machine learning (ML) tools for monitoring and operation of CO2 storage sites. Key
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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employment. Starting date: 10.02.2025 Job description:PhD position in atmospheric corrosion studies via novel experiments and machine learning Reference code: 50134137_2 ? 2025/MO 1 Commencement date: as soon
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parameter space of the electrolysis processes. DoE is required for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
the integration of ensemble machine learning models enhances the retrieval accuracy of phytoplankton biomass compared to traditional algorithms, and assess the implications for monitoring phytoplankton dynamics in
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of single-cell sequencing, machine learning, or an experimental background in (tumor) immunology would be advantageous. Due to the close connection to the Immune Monitoring Unit (NCT) and the University
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resource efficiency. A physics-based model for monitoring the condition of helicopter components is being developed as part of this project. With the help of flight test data, this model is to be calibrated
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. Ideally you have Programming skills and knowledge on machine learning and statistical data evaluation, creation of scientific programme codes using common software packages (MATLAB, Python, R) Simulation