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Deadline: none (posted 2025/05/13) Position Description: Position Description We are pleased to announce exciting postdoctoral opportunities within the Inverse Problems and AI group at Sun Yat-sen
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of inverse problems and computational imaging. Position The Vision Lab at the University of Antwerp (http://visielab.uantwerpen.be/ ) has an open position for a postdoctoral researcher to strengthen our
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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. This role is part of a collaborative research initiative involving academic partners and industry stakeholders. As the successful candidate you will lead efforts in membrane fabrication using phase-inversion
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contribute to teaching. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development
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potentially geophysics (inversion) to develop new exploration targeting methods. These will be designed to make the most of the excellent outcrop conditions in Saudi Arabia, which means that a particular focus
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Postdoc "Interferometric SAR Data Processing and Analysis for Implementation in the 3D-ABC Founda...
Area of research: Scientific / postdoctoral posts Job description: Postdoc "Interferometric SAR Data Processing and Analysis for Implementation in the 3D-ABC Foundation Model" (m/f/d) Background
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to best practices, and be applicable to large-scale survey results. The successful candidate will work at the intersection of machine learning, ethics, and social science, focusing on inverse reinforcement