Machine Learning Research Scientist – Optical & Physics-Based Modelling

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. Your work will focus on developing new ML formulations, models, and experimental approaches for modelling optical behaviour — including PSF fields, aberrations, flare and ghosting behaviour, wave-optics approximations, and other lens-driven effects. You will work at the research core of the optical modelling pipeline, designing algorithms that push the boundaries of optical ML and computational imaging.

Key Responsabilities: Foundational research and model development ●

Investigate and develop new physics-informed ML models for optical behaviour estimation and simulation. ●

Explore hybrid techniques combining classical optical modelling with neural approximators. ●

Develop novel techniques for modelling PSFs, Zernike fields, flare and ghost behaviour, scatter, and chromatic effects. Experimentation and prototyping ●

Build research prototypes to test new algorithms, architectures, and loss functions. ●

Evaluate performance using scientific benchmarks, simulation outputs, and rendering-based validation. ●

Maintain rigorous experiment tracking and reproducibility. Data and measurement interpretation ●

Analyse complex optical datasets (PSF stacks, Zernike maps, spectral captures, flare and ghost imagery). ●

Build data pipelines tailored to research experimentation. Collaboration and scientific contribution ●

Work closely with rendering, simulation, and software teams to transition research into production ML systems. ●

Develop technical documentation, research notes, and internal publications. ●

Keep abreast of research in computational imaging, scientific ML, neural optics, and physics-guided modelling, and contribute to internal R&D strategy and long-term modelling direction.

Skills and Experience: Required ● 

PhD (or equivalent experience) in Machine Learning, Computer Vision, Computational Imaging, Optical Engineering, or a closely related field. ●

Strong background in physics-informed ML, scientific ML, or applied modelling. ●

Proficiency with PyTorch, JAX, or TensorFlow. ●

Strong mathematical grounding: Fourier optics, PDEs, optimisation, linear algebra, and statistics. ●

Experience working with scientific or imaging data. ●

Fluent English, written and spoken (B2 or above). Bonus ●

Publications in ML, computational imaging, optics, or graphics. ●

Experience modelling PSFs, wave-optics, or image-formation physics. ●

GPU compute or high-performance ML skills. ●

Experience collaborating with teams in R&D-heavy or deep-tech environments.

What we offer: ● 

A research-driven role at the heart of the project’s modelling science. ●

Direct collaboration with optical scientists, ML researchers, and rendering engineers. ●

A fully remote, flexible working environment across Europe or the UK. ●

Competitive compensation commensurate with experience.