Postdoctoral Research Fellowship Positions in Bayesian Statistical Probabilistic Programming

We invite motivated and well-qualified researchers to work on Bayesian Statistical Probabilistic Programming, as part of a research programme funded by the National Research Foundation, Singapore.

The appointees will be hosted by the School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore. They will work in the Probabilistic Programming Lab, where research is carried out on a wide range of topics, especially in the interface of programming languages, machine learning, and Bayesian statistics, but also in allied areas in semantics of computation, formal methods and verification, and in logic and algorithms.

Bayesian Statistical Probabilistic Programming lies in the interface of programming languages, machine learning, and Bayesian statistics. These positions will suit researchers with expertise in one (or more) of the three areas, and are interested and committed to collaborating with experts in the other areas.

Job Description

The ultimate goal of our research programme is to develop provably correct and scalable general-purpose inference algorithms for programs in universal probabilistic programming languages (PPL).

Where possible, we seek results and insights that are generally applicable to Bayesian inference of nonparametric models, in a manner that is programming language neutral.

Our general objectives are:

1. Design new inference algorithms for nonparametric models
2. Devise new methods to verify and diagnose Bayesian inference methods
3. Develop optimising techniques for inference procedures and statistical tests

The appointees will work in one or more of the following research directions:

• Nonparametric inference algorithms for universal probabilistic programming: ICML21, ICML22, NeurIPS22
• Static analysis, verification and diagnostics of Bayesian inference: PLDI21, PLDI22a, LICS22
• Estimator efficiency: variance reduction and cost analysis: UAI22
• Optimisation techniques for inference engines
• Foundations, reasoning techniques and enabling methods for Bayesian statistic probabilistic programming: LICS21a, PLDI22b, ESOP21, LICS21b, LICS19

(For references, see Luke Ong's dblp page.)

The appointees will work closely with the PI, Prof. Luke Ong, and other team members.

Outstanding appointees may be granted the title of Research Assistant Professorship.

Essential Skills and Requirements

• Candidates must have a PhD (or be near completion) in Computer Science or Mathematics or Statistics or relevant discipline with a strong background in one or more of the following: semantics of computation, programming languages (lambda calculus and types, category theory), formal methods and verification, probability and measure theory, martingales, and machine learning
• Motivated, dedicated, and able to work both independently and collaboratively
• Strong publication record
• Willingness to learn and experiment with new techniques
• Ability to manage own academic research and associated activities
• Ability to contribute ideas to new research projects and research income generation
• Excellent communication skills, and fluency in English, both orally and in paper writing

Desirable Skills

The following skills are not essential, but desirable:

• Experience in functional programming
• Experience in algorithmic studies and experiments

Practical

This call was posted in October 2022. It will remain open until the two positions are filled.

A third postdoc research fellowship position will be advertised shortly.

Monthly salary: SGD 6000 - 12000 (depending on qualifications and experience). See xe.com for latest exchange rates.

The contract is initially for two years, with the possibility of extension of up to five years (until the project end date, Sep 2027).

Our admin colleagues will provide support for visa application, relocation, etc.

Starting date: as soon as possible.

Location: Singapore (remote work from abroad is not allowed).

Applications and Enquiries

To apply, please go to this webpage R00011754 Research Fellow (Computer Science/Mathematics/Statistics)_ P10025268 and R00011755 Research Fellow (Computer Science/Mathematics/Statistics)_ P10025269, and click the red APPLY button on the webpage. Please also email a copy of your application to the PI, Luke Ong (Email: luke.ong @ ntu . edu . sg).

If you have any questions about the research fellowship position, feel free to get in touch by emailing the PI, Luke Ong, with the subject: Research Fellowship Application (Bayesian Statistical Probabilistic Programming). It would be helpful if you could include the following:

• your CV
• brief description of research interests
• dblp or Google scholar link to your publications
• some representative papers
• (preferably) contacts of two references.