Company Description
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IDEADED is a Spanish deep-tech company with over 10 years of experience developing next-generation semiconductor technologies. We combine advanced research, design innovation, and process engineering to create solutions that go beyond traditional silicon and support Europe's technological sovereignty.
Driven by scientific excellence and industrial impact, IDEADED is building a more sustainable and competitive microelectronics ecosystem.
This is a full-time, on-site position for a highly motivated Senior Quantum Control Scientist to advance research in optimal quantum control. The successful candidate will develop theoretical and computational methods to design, optimize, and stabilize quantum operations for computing, simulation, and precision measurement.
This position requires expertise in quantum control theory, Hamiltonian engineering, and error correction/mitigation. Familiarity with machine-learning-driven control (reinforcement learning, Bayesian optimization, neural-network-assisted design) and proficiency in scientific programming environments are highly valued too.
The ideal candidate will demonstrate leadership in theory-experiment collaboration, a strong publication record, and enthusiasm for building a vibrant, interdisciplinary research program in quantum technologies.
D. in Physics, Applied Mathematics, or a closely related field is required.
Strong record of peer-reviewed publications and demonstrated research impact.
At least two years of postdoctoral research or relevant industrial experience in theoretical quantum control, quantum computing, or quantum information science.
Experience in: (i) optimal quantum control theory, focusing on the precise manipulation of quantum systems for high-fidelity gate design and robust quantum operations;
(ii) in error mitigation and quantum error correction through control-based strategies that enhance performance and scalability;
Experience in advanced numerical optimization techniques, such as: (i) Linear and nonlinear programming for constrained optimization of control fields, (ii) Gradient-based algorithms (e.G., GRAPE, Krotov, CRAB, automatic differentiation methods), (iii) Stochastic and evolutionary algorithms for high-dimensional or noisy landscapes, or (iv) Model-predictive and real-time feedback control strategies.
Experience in: Machine learning approaches to quantum control, including: (i) Reinforcement learning for adaptive pulse design and calibration and (ii) Bayesian optimization and neural-network-assisted control for robust parameter tuning. xqysrnh
Proficiency in computational tools and programming environments for optimization, machine learning, and quantum simulation (e.G., Python, Julia, C++, TensorFlow, PyTorch, Qiskit, or QuTiP).