Machine Learning Engineer
Pubblicato il 02-09-2026 - Dynamic Optics in Padova
ABOUT DYNAMIC OPTICS
Dynamic Optics is a CNR-IFN spin-off and technology leader in adaptive optics, specialising in measuring, analysing, and correcting the optical behaviour of real-world lenses. Based in Padova and backed by Officina Stellare SpA — an Italian leader in advanced optical instrumentation for space — Dynamic Optics combines academic novelty with industrial-grade engineering and real-world deployment.
We are at the centre of a pioneering deep-tech project, co-funded by the European Union, at the intersection of cinematography, optical engineering, imaging science, and machine learning. Its mission is to digitise the optical behaviour of real professional cinema lenses, enabling precise measurement, simulation, and creative manipulation of characteristics such as distortion, aberration, flare, bokeh, and depth of field. By combining advanced optical engineering, computational imaging, and AI-driven modelling, the project creates high-accuracy digital lens twins — bridging physical cinematography with digital image generation and unlocking new capabilities for VFX,
virtual production, CGI rendering, and generative AI.
With EU funding now secured, we are moving from a funded research action towards commercial deployment , and are building the team that will take this technology to the global audiovisual market.
THE ROLE
We are seeking a Machine Learning Research Scientist to drive the project’s foundational research into physics-guided, optical-system machine learning.
We are looking for a Machine Learning Research Scientist with a strong background in Computer Vision, 3D data processing, and AI for scientific imaging . The ideal candidate has experience developing deep learning models for image understanding, 3D representations, or computational imaging, and is interested in combining data-driven approaches with physics-based optical modelling.
Your work will focus on developing new ML formulations, models, and experimental approaches for modelling opti