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# Senior Algorithm Engineer (Phase Retrieval)

- **Company:** AIM Solutions
- **Location:** Eindhoven, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Big Data, Nvidia CUDA, Computer Programming, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Linux Development
- **Published:** November 12, 2026
- **Apply:** https://www.adzuna.nl/details/5961928090

## About the Role

* A PhD, or equivalent research depth from industry, in a physics-heavy or numerically heavy field
 * Several years developing numerical or scientific algorithms, ideally in imaging, inverse problems, or a comparable domain
 * Strong numerical optimization, both gradient-based and iterative methods
 * Working knowledge of Fourier optics and wave-propagation physics
 * Strong Python and array programming, with experience in GPU frameworks such as JAX, PyTorch, TensorFlow, or CUDA
 * You write tested, maintainable code that others can build on
 * You can reason from physics to algorithm and back, and judge what will improve a reconstruction
 * Technical curiosity; we will teach you the specifics of our computational imaging approach
 * Full professional proficiency in English

Bonus experience in any of these:

 * Phase retrieval, coherent diffractive imaging, holography, or tomography
 * Physics-informed machine learning, differentiable forward models, automatic differentiation
 * Regularization and constrained optimization for ill-posed inverse problems
 * High-performance and GPU computing with performance profiling
 * Linux development environments

## Description

* Develop and maintain our GPU-accelerated computational imaging engine for phase retrieval
 * Improve reconstruction quality, convergence, and robustness across varied and imperfect datasets
 * Design and implement new reconstruction methods: physics-based differentiable forward models, regularization and constraint schemes for ill-posed problems, and support for new acquisition modes
 * Translate methods from the literature and from our own ideas into tested, performant code
 * Use physical insight into the optical system to decide how to change the algorithm so that results improve
 * Profile and optimize the engine for GPU performance on large datasets