[ The Types Forum (announcements only), http://lists.seas.upenn.edu/mailman/listinfo/types-announce ]
> ==================================================================== > > PhD studentship: Microsoft Research PhD Scholarship: > Machine Learning and Natural Language Processing for Types in "Big Code" > > Supervisor: > Charles Sutton, University of Edinburgh > > Apply by 1 November for full consideration. > Later applications will still be considered if position unfilled. > > More information: > >* >http://www.ed.ac.uk/informatics/postgraduate/fees/research-scholarships/research-grant-funding/ms-research-phd-scholarship-machine-learning-nlp > * http://homepages.inf.ed.ac.uk/csutton/ > * Email Charles Sutton <csut...@inf.ed.ac.uk<mailto:csut...@inf.ed.ac.uk>> > > ==================================================================== > > Project Description > > The goal of this PhD studentship is to develop new machine learning > methods to predict facts about computer programs by combining > information from the source code with dynamic information from > runtime. One of the most common such facts are types of variables, > methods, etc; this information is so useful in preventing bugs that > programming languages like C# and Java require that programs contain > explicit annotations for the types of all program entities, even if > the type can be deterministically inferred from other information in > the program. > > But any annotation to a program comes with a cost to add the types to > a program and maintain them. Indeed, it is the desire to reduce this > cost has led to the popularity of non-statically typed languages such > as Python and JavaScript. More advanced research in programming > languages have developed rich languages for specifying more detailed > facts about programs, such as refinement types that allow for logical > constraints on variable values. These richer types can identify more > subtle bugs at compile time, but they come with a correspondingly > greater cost to add and maintain. > > This research project aims to obtain the benefits of rich type > annotations at lower cost, by developing machine learning methods to > automatically predict rich type annotations of programs that do not > contain explicit type annotations. We will develop new methods drawing > from probabilistic graphical models and deep learning to combine > information from the names in a program, from dynamic analysis, and > from the types of deterministic constraints used in traditional static > analysis. > > This studentship is an opportunity to combine cutting edge research in > machine learning, statistical natural language processing, and > programming languages. The project will be supervised by Dr Charles > Sutton at the University of Edinburgh, in collaboration with Dr Earl > Barr at UCL and Prof Andrew D Gordon of Microsoft Research. > > During the course of their PhD, the Scholar will be invited to > Microsoft Research in Cambridge for an annual PhD Summer School with > talks and poster sessions, which provides an opportunity to present > work to Microsoft researchers and Cambridge academics. > > What's required? > > The project is suitable for a student with a top MSc or first-class > bachelor's degree in computer science, statistics, physics, or a > related numerate discipline. Previous coursework or experience in > machine learning, statistical natural language processing, and > programming languages is desirable, although we do not expect students > to have all three of these. Because of the scale of the data set > involved, a strong programming background will be essential for this > project. > > Our research group > > The School of Informatics at the University of Edinburgh has one of > the largest concentrations of computer science research in Europe, > with over 100 faculty members and 275 PhD students. The school is > particularly strong in the three research areas most relevant to this > project, machine learning, natural language processing, and > programming languages. Our strength in these areas have been > recognized by awards of EPSRC Centres for Doctoral Training in > Pervasive Parallelism and in Data Science - this project cuts across > the remit of these two centres. The University of Edinburgh is one of > the founding partners of the Alan Turing Institute, the UK's national > research institute for data science. For more information on the > research in Dr Sutton's group, see: > http://homepages.inf.ed.ac.uk/csutton/ > > Initial enquiries > > For informal enquiries about the studentship, please contact Dr > Charles Sutton. Formal application must be through the School's normal > PhD application process: > http://www.ed.ac.uk/schools-departments/informatics/postgraduate/apply > Select the Informatics: Institute for Adaptive and Neural Computation > research area. > > Application deadline > > For full consideration, please apply by 1 November 2016. We will aim > to fill the studentship as soon as possible, so that the successful > applicant can begin in the spring semester 2017. > > Funding Notes > > The Microsoft scholarship consists of an annual bursary up to a > maximum of three years. > This is a fully funded studentship for UK and EU students. For > overseas applicants, we can provide funding for stipend and for fees > only to the UK/EU level. The remaining fees component will need to > come from another source. Overseas applicants are advised to apply > before the standard Informatics deadlines and apply for other > scholarships.