539 high-performance-quantum-computing Postdoctoral positions in mechanical-engineering
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Responsibilities work on the research projects related to photonic or neutral atom quantum computing, and quantum information/simulation under co-supervision of Profs. Zidan Wang & Xianmin Jin coordinate theoretical
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will work together with two experimentally oriented PhD’s who already started. Postdoc: Computational Modeling of Ink Performance Model the Mechanics of Tomorrow’s Inks This position focuses
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to establish a low-emission, end-to-end pathway for converting challenging scrap streams into high-performance aluminium alloys. CLEANMAT — About the Horizon Europe Project on Clean Aluminium Material Processing
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Help accelerate the electrification of industry by combining molecular and process-scale simulations for high-temperature thermal energy storage. Job description Carbon-based chemicals are essential
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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18 Sep 2026 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Other Economics » Other Engineering » Control engineering Engineering » Systems
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. Qualifications Essential Qualifications: PhD in computer science/engineering with expertise in a security or privacy research topic. Less than 5 years post-receiving the Doctoral degree. Excellent analytic, design
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theoretical particle physics, nuclear physics and technology, particle astrophysics and cosmology, quantum computing, technologies associated with particle detectors, accelerators, synchrotron light sources and
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We invite applications for a 2-year Postdoctoral Research Associate position in Nucleic Acid Computing and Molecular Programming in the laboratory of Dr Gamze Gursoy at the University of Cambridge
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Requisition Id 17070 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifting frameworks for high