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Postdoc (f/m/d) in Quantum Algorithms for Fluid and Environmental Flow Modelling

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⚡ Quantum Brief
A Helmholtz Quantum Use Challenge project (qFLOW) is hiring postdoctoral researchers to develop quantum algorithms for fluid dynamics and environmental modeling, focusing on PDE/ODE-based systems. The role prioritizes algorithmic innovation over quantum advantage demonstrations. Research will explore variational quantum algorithms (QITE/VarQITE), quantum lattice Boltzmann methods, and hybrid quantum-classical approaches like tensor networks and physics-informed machine learning. Testbeds include groundwater hydrology and multiphase fluid dynamics. The position is part of a collaboration between four Helmholtz centers: HZDR, UFZ, DESY, and FZJ, offering access to HPC, GPU emulation, and quantum hardware (IBM Quantum, JUNIQ). Applications close February 19, 2026. The role sits within the AI 4 Quantum group, specializing in machine learning for quantum simulation and computing. Candidates will work on hybrid quantum-classical solutions, bridging quantum computing with environmental and fluid dynamics challenges. No prior quantum hardware experience is specified as a requirement.
Postdoc (f/m/d) in Quantum Algorithms for Fluid and Environmental Flow Modelling

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Postdoc (f/m/d) in Quantum Algorithms for Fluid and Environmental Flow Modelling Application deadline: Thursday, February 19, 2026Research group: AI 4 Quantum - Machine Learning for Quantum Simulation and Quantum ComputingEmployer web page: Application portalJob type: PostDocThe qFLOW project (Helmholtz Quantum Use Challenge) is recruiting postdoctoral researchers to work on quantum and hybrid quantum–classical algorithms for PDE- and ODE-based models arising in fluid dynamics and environmental systems. The project focuses on algorithmic development rather than quantum advantage demonstrations on large-scale applications. Current research directions include variational quantum algorithms (e.g. QITE/VarQITE), quantum lattice Boltzmann methods, quantum-inspired tensor network approaches for PDEs, and physics-informed machine learning with exploratory links to QML. Application testbeds come from groundwater hydrology and multiphase fluid dynamics. qFLOW is a collaboration between Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Centre for Environmental Research (UFZ), Deutsches Elektronen-Synchrotron (DESY), and Forschungszentrum Jülich (FZJ), with access to HPC infrastructure, GPU-based quantum emulation, and quantum hardware (IBM Quantum, JUNIQ). Log in or register to post comments

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Source: Quantiki