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, United States of America [map ] Appl Deadline: (posted 2025/10/28, listed until 2026/04/28) Position Description: Apply Position Description The Department of Mathematics at the Courant Institute is calling for applications
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5 Jan 2026 Job Information Organisation/Company IFP Energies nouvelles (IFPEN) Research Field Geosciences » Hydrology Mathematics » Computational mathematics Physics » Computational physics
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Pohang University of Science and Technology (POSTECH) | Republic, Kansas | United States | 2 months ago
Pohang University of Science and Technology (POSTECH), Mathematics Position ID: 437-LECTURER [#27521] Position Title: Position Location: Pohang-Si, Gyeongsangbuk-do 37673, Korea, The Republic
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, China [map ] Subject Areas: mathematics, statistics, or related interdisciplinary fields Appl Deadline: 2025/11/18 11:59PM (posted 2025/10/19, listed until 2025/11/19) Position Description: Apply
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | 4 days ago
): - Cover letter, - CV, - 2 Reference contacts (please do not send generic letters, contact details only). Links to the Membrane Machines Laboratory: https://www.membranemachines.com/ https://imol.institute
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | 4 days ago
publications, - 2 Reference contacts (please do not send generic letters, contact details only). Links to the Membrane Machines Laboratory: https://www.membranemachines.com/ https://imol.institute/leaders/de
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15 Jan 2026 Job Information Organisation/Company Lodz University of Technology Department Faculty of Technical Physics, Information Technology, and Applied Mathematics, Institute of Physics Research
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15 Jan 2026 Job Information Organisation/Company Lodz University of Technology Department Faculty of Technical Physics, Information Technology, and Applied Mathematics, Institute of Physics Research
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learning to automatically build models from experimental data while directly embedding physical and mathematical knowledge within the learning architecture. This hybrid approach produces more reliable models
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learning to automatically build models from experimental data while directly embedding physical and mathematical knowledge within the learning architecture. This hybrid approach produces more reliable models