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time, gross annual salary of EUR 30,456.00. Application deadline: Tuesday, 29 September 2026, 13:00 (Italian time). Applications must be submitted online via the PICA platform: https://pica.cineca.it
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techno-economic analysis, life-cycle assessment, mathematical optimization, process-oriented modeling of industrial energy systems, and the analysis of regulatory frameworks. In particular, we focus on
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 14 hours ago
areas: computer vision, computer graphics, and deep learning. We require documented experience about computer vision, machine learning, deep learning or optimization, demonstrated by high-quality
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our community. We are now looking for a Doctoral Researcher in AI and Quantum-Inspired Optimization for Sustainable Energy Systems Are you passionate about developing AI-based and quantum-inspired
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optimization of quantum algorithms for hybrid quantum simulators. Applicants should have a PhD in Physics, Chemistry, Computer Science, or a closely related field. To apply, a CV, a brief statement of
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optimization of quantum algorithms for hybrid quantum simulators. Applicants should have a PhD in Physics, Chemistry, Computer Science, or a closely related field. To apply, a CV, a brief statement of
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optimization of quantum algorithms for hybrid quantum simulators. Applicants should have a PhD in Physics, Chemistry, Computer Science, or a closely related field. To apply, a CV, a brief statement of
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optimization of quantum algorithms for hybrid quantum simulators. Applicants should have a PhD in Physics, Chemistry, Computer Science, or a closely related field. To apply, a CV, a brief statement of
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https