Dear Cafe, 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. Here are the official adverts: http://ig5.i-grasp.com/fe/tpl_UniversityOfLeicester01.asp?newms=jj&id=85616&aid=14178 http://ig5.i-grasp.com/fe/tpl_UniversityOfLeicester01.asp?newms=jj&id=85615&aid=14178 The application deadline is April 10. If you think you may be interested, you are welcome to ask me (tanielsen@gmail.com or tomn@openbrain.org) or Tom Matheson (tm75@le.ac.uk) any questions. Tom Nielsen OpenBrain Ltd http://openbrain.co.uk