We invite applications for a PhD position focusing on the development and validation of a real-time divertor detachment control diagnostic for the JT-60SA tokamak and ITER, building on concepts and experience from EU-DEMO diagnostic developments. The project targets visible (450–750 nm) divertor spectroscopy as a control observable to detect and prevent loss of detachment, with the longer-term goal of supporting DEMO-relevant control strategies in reactor-like scenarios.
The successful candidate will work on the end-to-end chain from physics modelling to diagnostic implementation, including: (i) review and optimisation of spectral control signals (e.g. Balmer ratios such as Dα/Dγ and impurity emission lines with neon as baseline seeded species), (ii) simulation and interpretation of line-integrated signals using SOLPS and/or collisional–radiative (CR) modelling to map measured ratios to local plasma parameters (Tₑ, nₑ, recombination fraction), (iii) assessment of line-of-sight geometry and photon budgets to achieve the required temporal resolution and latency, and (iv) contribution to the specification of a multi-channel polychromator system coupled to fibres and a real-time DAQ/FPGA-capable processing chain. The work will include using the existing JT-60SA divertor visible-spectrometer lines of sight for proof-of-principle.
Candidates should have a strong MSc-level background in plasma physics with practical skills in Python (data analysis/modelling) and a willingness to engage with hardware interfaces (detectors, electronics, DAQ). Experience with spectroscopy instrumentation, or FPGA/real-time systems is an advantage but not required. The position offers close collaboration with diagnostic engineers and physicists, access to international fusion programmes, and opportunities to contribute to publications, conference presentations, and design reviews.
Good command of English language Programming skills Availability to participate in European and optionally in Asian fusion experiments for several weeks. Advantage: experience in python coding, data analysis

