Inverse Problems Jobs
Computer vision roles requiring Inverse Problems expertise, across all industries and experience levels.
Open Positions
What is Inverse Problems?
Inverse problems recover an unknown cause from observed effects — an image from blurred, noisy or partial measurements. They are typically ill-posed, meaning solutions may not be unique or stable, so regularisation and priors do the heavy lifting.
Where Inverse Problems is used
CT and MRI reconstruction, deblurring, seismic and non-destructive testing, and microscopy deconvolution all reduce to inverse problems, and this is the theoretical frame most computational imaging work sits inside.
Roles that ask for Inverse Problems
- Computational Imaging Scientist
- Research Scientist, Imaging
- Medical Imaging Scientist
- Algorithm Engineer
- Applied Mathematician
Related skills & tools
Inverse Problems jobs — common questions
What does ill-posed mean in practice?
That small measurement errors can produce wildly different reconstructions. The practical consequence is that you never solve the problem directly — you add a regulariser encoding what plausible solutions look like, and the choice of prior largely determines the result.
How do learned methods fit the classical framework?
Increasingly as learned priors inside a classical solver — plug-and-play denoisers, diffusion priors and unrolled optimisation — rather than as end-to-end black boxes. This keeps the physics of the forward model explicit, which regulated domains require.
Is a PhD necessary?
More often than in most of computer vision. The work is mathematically demanding and concentrated in medical imaging vendors, national labs and instrument companies, where research credentials are the norm.