Two research positions in functional probabilistic programming
Two positions involving substantial Haskell work are available for a project that seeks to combine functional and probabilistic programming. We are looking for two postdocs to work on a practical system for large-scale inference in scientific and clinical datasets using bayesian statistical models, embedded in a typed functional programming language and based on stochastic dynamical systems.
* A typed hierarchical database that uses a Hindley-Milner-like typesystem (with records) to organise large, complex and heterogeneous data from a hospital.
* Probabilistic inference over these complex datasets
* Parallelizing Bayesian inference
* Modelling clinical datasets (for instance ECG) using dynamical systems.
Some of these ideas have been explored in our Baysig Language (http://tinyurl.com/Baysig) and BayesHive project (https://BayesHive.com) - both Baysig and BayesHive are implemented in Haskell. However, these are academic research posts and there is scope for exploring different designs to meet the same aims.