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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Use of effective theories for the precise description of thermal phase transitions and the resulting
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. A special emphasis will be placed on developing theory that goes beyond the steady-state behavior of dynamical systems. You will also have the opportunity to collaborate with PhD students working
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 1 day ago
Job Code: 15-2026 Job Offer from September 24, 2026 The Max Planck Institute for Nuclear Physics is one of 85 research institutes of the Max Planck Society for the Advancement of Science e.V. (MPG
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-on programming skills This is an engineering-heavy PhD. We expect code that another person can clone, run and reproduce — not notebooks that worked once. Must have: Python (professional level). Type hints
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internally at the University of Sydney and externally across multiple institutions publish research papers on mathematical theory and the development and analysis of algorithms explore how artificial
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is seeking a highly motivated PhD candidate to work on a fundamental research project on systems and control theory for learning in neuromorphic circuits. Neuromorphic computing is an analog, brain
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PhD at the InnoRenew CoE, Andrej Marušič Institute. The successful candidate will be actively involved in research activities, particularly those related to the development and scientific use of the HB
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or equivalent in physics or other cognate area of relevance to the post. Hold or about to obtain a PhD in a relevant area (e.g. Physics). Significant relevant experience in the in the following areas: The theory
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Date Posted: 09/18/2026 Req ID: 50256 Faculty/Division: Faculty of Arts & Science Department: Dept of Chemistry Campus: St. George (Downtown Toronto) Description: Course Title and Code: CHM427H1
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twins, optimization, and control. In this PhD project, you will develop a new systems and control theory for learned operators, bridging modern scientific machine learning with classical control theory