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Position Description Postdoctoral positions in theory in biological physics, soft matter, statistical mechanics, complex systems and related research fields are available in the research group of Dr
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years. The project involves the synthesis and characterisation of molecular lanthanide complexes to develop physical inorganic principles for controlling optical linewidths and oscillator strengths in
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Description: A Postdoctoral Associate position in theoretical and computational quantum information science and condensed matter physics is available in the group led by Prof. Haining Pan in the Department
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5 Sep 2026 Job Information Organisation/Company CNRS Department Laboratoire de Chimie Moléculaire Research Field Chemistry Physics Researcher Profile First Stage Researcher (R1) Application Deadline
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detailed structural information on active pharmaceutical ingredients (APIs), pharmaceutical cocrystals and salts, formulated drug products, and other complex solid-state systems. Projects will utilize state
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responsibility for carrying out research on complexity lower bounds that combines approaches from algebraic complexity, proof complexity and meta-complexity. This is part of an EPSRC-funded project on Integrated
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. Application Process The University of Oxford is committed to equality and valuing diversity. All applicants will be judged on merit, according to the selection criteria. Applications for this vacancy should be
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or integrating control algorithms into physical testbeds Knowledge of predictive maintenance methods and anomaly detection techniques for complex systems Experience with sensor networks, IoT platforms, or real
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Education & Experience: A Ph.D. in Physical Oceanography, Ocean Engineering, Ocean or Atmospheric Sciences, Physics, Applied Physics, Earth Science, Applied Mathematics, or a closely related field is required
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computationally efficient approaches to progressive damage modelling. It is natural for you to think in terms of hybrid modelling strategies that combine physics-based simulations with machine learning when