We have an opening for a post-doctoral scholar to work on methods for and applications of hierarchical statistical models using the NIMBLE software (https://r-nimble.org) at the University of California, Berkeley. NIMBLE is an R package that combines a new implementation of a model language similar to BUGS/JAGS, a system for writing new algorithms and MCMC samplers, and a compiler that generates C++ for each model and set of algorithms. The successful candidate will work with Chris Paciorek, Perry de Valpine, and potentially other NIMBLE collaborators to pursue a research program with a combination of building and applying methods in NIMBLE. Specific methods and application areas will be determined based on interests of the successful candidate. Applicants should have a Ph.D. in Statistics or a related discipline. We are open to non-Ph.D. candidates who can make a compelling case that they have relevant experience. The position is funded for two years, with an expected start date between October 2018 and June 2019. Applicants should send a cover letter, including a statement of how their interests relate to NIMBLE, the names of three references, and a CV to firstname.lastname@example.org, with “NIMBLE post-doc application” in the subject. Applications will be considered on a rolling basis starting 15 October, 2018.
There will be a three-day NIMBLE workshop in Sempach, Switzerland, 23-25 April, hosted at the Swiss Ornithological Institute. More information can be found here: http://www.phidot.org/forum/viewtopic.php?f=8&t=3586. Examples will be oriented towards ecological applications, but otherwise the workshop content will be general.
The NIMBLE statistical software project at the University of California, Berkeley is looking for a post-doc or statistical software developer. NIMBLE is a tool for writing hierarchical statistical models and algorithms from R, with compilation via code-generated C++. Major methods currently include MCMC and sequential Monte Carlo, which users can customize and extend. More information can be found at https://R-nimble.org. Currently we seek someone with experience in computational statistical methods such as MCMC and excellent software development skills in R and C++. This could be someone with a Ph.D. in Statistics, Computer Science, or an applied statistical field in which they have done relevant work. Alternatively it could be someone with relevant experience in computational statistics and software engineering. The scope of work can include both core development of NIMBLE and development and application of innovative methods using NIMBLE, with specific focus depending on the background of the successful candidate. Applicants must have either a Ph.D. in a relevant field or have a proven record of relevant work. Please send cover letter, CV, and the names and contact information for three references to email@example.com. Applications will be considered on a rolling basis starting 30 January, 2018.
This position includes work to harness parallel processing and automatic differentiation, to generate interfaces with other languages such as Python, to improve NIMBLE’s scope and efficiency for large statistical models, and to build other new features into NIMBLE.
The work will involve programming in R and C++, primarily designing and implementing software involving automated generation of C++ code for class and function definitions, parallel computing, use of external libraries for automatic differentiation and linear algebra, statistical algorithms and related problems. The position will also involve writing documentation and following good open-source software practices.
See here to apply.
NIMBLE version 0.6-1 has been released on CRAN and at r-nimble.org.
NIMBLE is a system that allows you to:
- Write general hierarchical statistical models in BUGS code and create a corresponding model object to use in R.
- Build Markov chain Monte Carlo (MCMC), particle filters, Monte Carlo Expectation Maximization (MCEM), or write generic algorithms that can be applied to any model.
- Compile models and algorithms via problem-specific generated C++ that NIMBLE interfaces to R for you.
Most people associate BUGS with MCMC, but NIMBLE is about much more than that. It implements and extends the BUGS language as a flexible system for model declaration and lets you do what you want with the resulting models. Some of the cool things you can do with NIMBLE include:
- Extend BUGS with functions and distributions you write in R as nimbleFunctions, which will be automatically turned into C++ and compiled into your model.
- Program with models written in BUGS code: get and set values of variables, control model calculations, simulate new values, use different data sets in the same model, and more.
- Write your own MCMC samplers as nimbleFunctions and use them in combination with NIMBLE’s samplers.
- Write functions that use MCMC as one step of a larger algorithm.
- Use standard particle filter methods or write your own.
- Combine particle filters with MCMC as Particle MCMC methods.
- Write other kinds of model-generic algorithms as nimbleFunctions.
- Compile a subset of R’s math syntax to C++ automatically, without writing any C++ yourself.
Some early versions of NIMBLE were not on CRAN because NIMBLE’s system for on-the-fly compilation via generating and compiling C++ from R required some extra work for CRAN packaging, but now it’s there. Compared to earlier versions, the new version is faster and more flexible in a lot of ways. Building and compiling models and algorithms could sometimes get bogged down for large models, so we streamlined those steps quite a lot. We’ve generally increased the efficiency of C++ generated by the NIMBLE compiler. We’ve added functionality to what can be compiled to C++ from nimbleFunctions. And we’ve added a bunch of better error-trapping and informative messages, although there is still a good way to go on that. Give us a holler on the nimble-users list if you run into questions.
Our paper giving an overview on the rationale and design of NIMBLE has appeared online in accepted manuscript form at the Journal of Computational and Graphical Statistics. You can get it here.
We’ve just released the next major version of NIMBLE.
- more efficient computations for conjugate sampling,
- additional automated checking of BUGS syntax to improve NIMBLE’s warning/error messages,
- new API functionality to allow the use of syntax such as model$calculate(), etc. (syntax such as calculate(model) still works),
- new API functionality for MCMC sampler specification,
- improvements in speed and memory use in building models,
- addition of forwardsolve, backsolve, and solve to the NIMBLE DSL, and
- a variety of other items.
More details in the NEWS file that accompanies the package.
We anticipate being on CRAN in coming weeks and a next release soon that will include a full suite of sequential Monte Carlo (i.e., particle filtering) algorithms.
We have a 1-year opening for a post-doc interested in developing statistical methods in NIMBLE.
Here is the official, approved job advertisement:
POSTDOCTORAL SCHOLAR POSITION AVAILABLE IN COMPUTATIONAL STATISTICS – UNIVERSITY OF CALIFORNIA, BERKELEY
The Departments of Statistics and Environmental Science Policy, and Management have an opening for a Postdoctoral Scholar – Employee to develop and apply statistical algorithms as part of the NIMBLE software development team. NIMBLE is a NSF-funded framework for programming computational methods for general hierarchical models such as Markov chain Monte Carlo, sequential Monte Carlo, and numerical integration and approximation. More information is available at R-nimble.org. The post- doc will be supervised by co-PIs Perry de Valpine and Chris Paciorek. We seek a candidate who will build out NIMBLE’s algorithm library, which includes using it as a platform for methodological and applied research. The successful candidate will be expected to author peer-reviewed publications and contribute to software development.
Candidates must have completed all degree requirements except the dissertation or be enrolled in an accredited PhD or equivalent degree in a statistical field such as Statistics or Computer Science or a field of statistical application at the time of application.
Candidates must have a PhD or equivalent degree in a statistical field such as Statistics or Computer Science or in a field of statistical application such as biology, ecology, environmental science, political science, psychology, education, public health or related field by appointment start date.
Demonstrated experience programming complex scientific computing applications using R and/or C++, Python or others. Demonstrated experience advancing computational statistical methodology by appointment start date.
The position is available to start immediately but we seek the best candidate even if they cannot start until a later date. The initial appointment is for one-year, with renewal based on performance and funding. This is a full-time appointment.
SALARY AND BENEFITS
Salary will be commensurate with qualifications and experience. Generous benefits are included (http://vspa.berkeley.edu/postdocs)
Interested individuals should include a 1-2 page cover letter describing their research experience and publications along with a current CV and the names and contact information of three references. Letters of reference may be requested for finalists. It is optional to include a statement addressing past and/or potential contributions to diversity through research, teaching, and/or service.
This position will remain open until filled.
Questions regarding this recruitment can be directed to Maria P. Aranas, firstname.lastname@example.org.
All letters will be treated as confidential per University of California policy and California state law. Please refer potential referees, including when letters are provided via a third party (i.e. dossier service or career center) to the UC Berkeley Statement of Confidentiality (http://apo.berkeley.edu/evalltr.html ) prior to submitting their letters.
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy see: http://policy.ucop.edu/doc/4000376/NondiscrimAffirmAct
The Department is interested in candidates who will contribute to diversity and equal opportunity in higher education through their research or teaching.
The University of California, Berkeley has an excellent benefits package as well as a number of policies and programs in place to support employees as they balance work and family.