Postdoc, Information Theory, Probability, and Statistics Unit

We are seeking a highly motivated and skilled Postdoctoral Researcher to join our unit. The successful candidate will work closely with the head of the unit and team to conduct research, publish findings, and contribute to the advancement of our research agenda. The work is interdisciplinary and spans various fields: Information Theory, Probability Theory, Statistics, Learning Theory, Biology, etc.
The main areas of interest include (but are not restricted to) the following:
- information measures;
- concentration of measure;
- concentration of measure in non-classical settings (non iid);
- information-theoretic bounds on the generalisation error of learning algorithms;
- estimation theory;
- hypothesis testing in non-classical settings;
- estimation and prediction in biological settings;
- properties (contraction) of Markovian and non-Markovian operators on probability measures;
- variational characterisations of information measures;
- computational neuroscience.