Modern photonic devices, crucial for applications from manufacturing to computing, are often limited by the static nature of optical elements. This project pioneers a novel approach for adaptive photonics, targeting the design of photonic systems such as dynamic laser beam routers enabled by intense, configurable sound fields that dynamically shape optical properties in gas-phase media. This PhD will concentrate on the numerical foundations for the design, optimization, and real-time control of adaptive sono-photonic systems, empowering future experimental realizations reaching far beyond recent pioneering works [Schrödel et al., Nature Photonics 2024, https://doi.org/10.1038/s41566-023-01304-y].

The core challenge lies in developing highly efficient computational models and robust optimization strategies in highly multi-dimensional optimization spaces. We will leverage advanced numerical optimization, augmented by machine-learning principles, to create an auto-differentiable framework describing the acousto-optic interaction. This framework will form the basis for developing a Model Predictive Control (MPC) system. Considering the expected inherent discrepancies between simulations and real-world experiments, MP is chosen for its ability to handle model uncertainties and adapt to changing conditions, where the efficient solution of underlying optimal control problems is key.

This research will establish new computational tools and optimization algorithms for reconfigurable optical systems, enabling dynamic, precise, and damage-immune routing of high-power laser beams by adjusting ultrasound transducer parameters in real-time. The project sits at the intersection of photonics, acoustics, numerical optimization, data science, and control theory, aiming to lay the numerical groundwork for next-generation programmable photonic devices.

Requirements:

• required qualifications
• Master's degree (or equivalent) in physics, applied mathematics, computer science, engineering, or a related computational field
• proficient programming skills in a high-level language, with a strong preference for Python and its scientific computing ecosystem (e.g., NumPy, SciPy)
• demonstrated experience in either (a) the numerical simulation of physical systems (e.g., from a Master's thesis or a significant project) or (b) the application of numerical optimization techniques
• a strong motivation to learn and work at the intersection of both fields
• desirable qualifications
• familiarity with machine learning concepts or frameworks (e.g., PyTorch, JAX)
• previous exposure to control theory concepts
• experience in a high-performance computing (HPC) environment

Position:

• Deutsches Elektronen-Synchrotron DESY
• 75% EGR. 13 (TVöD) position for three years

At DESY, gender equality is an important aspect. To support work-life balance, we offer flexible working hours, variable part-time, job-sharing models, and participation in mobile work (up to 50%).

DESY promotes equal opportunities and diversity. The professional development of women is very important to us. Therefore, we strongly encourage women to apply for the vacant position.

Applications from severely disabled persons will be given preference if they are equally qualified (sbv.desy.de).