The Oxford group has positions for a Doctor of Philosophy student and a Postdoctoral Fellow.

The Postdoctoral position is available immediately. Further details (please ignore the "applications closed" message).

The DPhil position, starting in the next academic year (October 2021), is funded by a Marie Curie Innovative Training Network, which includes generous travel funding and several internships at leading European research institutes.

Group site

For more information, please contact Prof. Alexander Lvovsky


Research projects

1. Superresolution imaging via linear optics in the far-field regime
Rayleigh's criterion defines the minimum resolvable distance between two incoherent
point sources as the diffraction-limited spot size. Enhancing the resolution beyond this
limit has been a crucial outstanding problem for many years. A number of solutions have
been realized; however, all of them so far relied either on near-field or nonlinear-optical
probing, which makes them invasive, expensive and not universally applicable. It would
therefore be desirable to find an imaging technique that is both linear-optical and
operational in the far-field regime. A recent theoretical breakthrough demonstrated that
"Rayleigh's curse" can be resolved by coherent detection the image in certain transverse
electromagnetic modes, rather than implementing the traditional imaging procedure,
which consists in measuring the incoherent intensity distribution over the image plane. To
date, there exist proof-of-principle experimental results demonstrating the plausibility of
this approach. The objective of the project is to test this approach in a variety of settings
that are relevant for practical application, evaluate its advantages and limitations. If
successful, it will result in a revolutionary imaging technology with a potential to change
the faces of all fields of science and technology that involve optical imaging, including
astronomy, biology, medicine and nanotechnology, as well as optomechanical industry.

See https://arxiv.org/abs/2105.01743 for more details

2. Optical neural networks
Machine learning has made enormous progress during recent years, entering almost all
spheres of technology, economy and our everyday life. Machines perform comparably to,
or even surpass humans in playing board and computer games, driving cars, recognizing
images, reading and comprehension. It is probably fair to say that a modern machine will
perform better than a human in any environment it has complete knowledge of. These
developments however impose growing demand on our computing capabilities, including
both the size of neural networks and the processing rate. This is particularly concerning
in view of the decline of Moore's law.

The project is to implement artificial neural networks using optics rather than electronics.
The training of neural network consists of linear operations (matrix multiplication)
combined with nonlinear activation functions applied to individual units. Both these
operations can be implemented optically using lenses, spatial light modulators and
nonlinear optical techniques such as saturable absorption. However, one crucial element
of the training procedure - so-called backpropagation - has so far remained elusive. Our
group has developed an idea to overcome this obstacle and implement pure optical
backpropagation in a neural network, thereby enabling the training that is practically
electronics-free. We confirmed the viability of this approach by simulation. Our next goal
– and the goal of this doctoral research project – is to set up an experiment and test the
method in a practical setting.

See https://arxiv.org/abs/2009.12095 and https://arxiv.org/abs/1912.12256 for more details