Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
a Research Infrastructure? No Offer Description The Chair of Nonlinearity Engineering (NLE) explores both the fundamental principles and practical implementation of how nonlinear systems can be
-
holder is required to: Design and perform rigorous and systematic experiments to explore new ultrafast nonlinear optics phenomena in gas-filled hollow-core optical fibres. Work with ultrafast optical
-
either laser and optical physics, particle beam physics, strong-field QED or plasma physics is recommended. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7639-SEBCOR-001
-
the physics of engineered light-matter interaction in bosonic cQED systems. We are a diverse team of scientists doing small-scale, curiosity-driven experiments to better understand the rich nonlinear dynamics
-
11 Nov 2025 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United Kingdom
-
) to work on topics in quantum information theory and quantum technologies, with a special focus on optical quantum computing and machine learning with nonlinear mechanical systems. Your personal sphere
-
editing; Proficiency with nonlinear editing systems such as Adobe Premiere Pro, Avid or Final Cut Pro; Experience mentoring for career development and student media; Evidence of ability to collaborate with
-
. Studying their nonlinear dynamic behaviour is challenging, due to their complexity and the many parameters at play, but crucial to understand important phenomena, such as cellular dynamics, onset of diseases
-
to control engineering, electric vehicles, hybrid powertrains, and/or fuel cell systems or equivalent competence. Working experience on dynamic programming, mixed integer nonlinear programming, model
-
(Dakić Group) to work on topics in quantum information theory and quantum technologies, with a special focus on optical quantum computing and machine learning with nonlinear mechanical systems. Your