a project funded by the Australian Research Council, titled
"Decomposition and Duality: New Approaches to Integer and Stochastic
Integer Programming".
The project is a collaboration of Professors Andrew Eberhard (RMIT,
Australia), Natashia Boland (Georgia Institute of Technology) and Jeff
Linderoth (University of Wisconsin, Madison).
The position is based at RMIT University, School of Mathematical and
Geospatial Sciences, in Melbourne, Australia, under the supervision of
Prof. Eberhard, in collaboration with Profs Boland and Linderoth.
The Postdoctoral Fellowship will be paid under ARC Discovery Project
DP140100985. The successful candidate will be employed at a level in the
range A6-B1, depending on experience and career stage, which comes with
remuneration of $75,172-$84,934 per annum.
The appointment is initially for 1 year. Funding is available for an
additional year by mutual agreement.
To view the position description and find instructions as to how to
lodge your application, go to
http://yourcareer.rmit.edu.au/caw/en/job/550585/post-doctoral-research-fellow.
Applications close on Sunday 7th June 2015. Applicants are requested to
provide a separate document that briefly addresses each of the key
selection criteria as outlined in the Position Description.
For enquiries, please contact
Prof. Andrew Eberhard
andy.eberhard@rmit.edu.au
+61 3 9925 2616
Background:
The successful applicant will join a team of researchers at RMIT
University working on of this project. This fellowship will be
administered at RMIT and the candidate will work under the
supervision of Prof. Andrew Eberhard. One of Australia's original
educational institutions founded in 1887, RMIT is now the nation's
largest tertiary institution. The School of Mathematical &
Geospatial Sciences draws together disciplines involving the
collection of data with the analysis of data and the understanding
and optimisation of systems through modelling and visualisation.
Project aims:
Because of their rich modelling capabilities, integer programs are
widely used in industry for decision making and planning. However
their solution algorithms do not have the maturity of their cousins
in convex optimization, where the theory of strong duality is
ubiquitous. Efficient methods for convex optimization under
uncertainty do not apply to the integer case, which is highly
nonconvex. Furthermore integer models usually assume the data is
known with certainty, which is often not the case in the real world.
This project looks towards the development of new theory and
algorithms to enhance the analysis of integer models, including
those that incorporate uncertainty, while also exploiting the use of
parallel computing paradigms.
Desired Skill Set:
The successful candidate will hold a PhD in optimization, operations
research, computer science, or closely related discipline, with strong
advanced mathematical and computer programming skills. Research
experience in some aspect of optimization theory and/or algorithms
is essential, as are skills in parallel computing, in particular, the
implementation and computational performance assessment of
parallel algorithms. Knowledge of integer programming and
stochastic programming theory and techniques is highly desirable.
--
Jeff Linderoth, Professor
Dept. of Industrial and Systems Engineering
University of Wisconsin-Madison
O: 608-890-1931
http://www.engr.wisc.edu/ie/faculty/linderoth_jeffrey.html
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