Postdoc position at SUTD

A 2-year postdoc position is available at the Singapore University of
Technology and Design.

This position is part of a research project aimed at integrating
optimization with design practice in sustainable architecture. The
specific research area is the optimization of expensive black-box
functions, in particular optimizing the outcome of (highly nonlinear)
simulators that have continuous and discrete input parameters. This is
a challenging applied research project with important applications. It
involves collaboration with renown architects on real-world buildings,
with the potential of having an impact on design practice.

We are looking for motivated applicants with a strong theoretical and
computational background. The ideal candidate should be skilled in at
least one of: global optimization, mixed-integer nonlinear
programming, and related areas.

The position comes with an attractive remuneration package,
competitive on an international level, as well as travel support. The
start date is flexible, ideally between August 2012 and January 2013.
Interested applicants are encouraged to send a CV with a brief summary
of past research and names of references - all in PDF format - to:
Giacomo Nannicini, nannicini@sutd.edu.sg. All questions can be
directed to the same address.

Scientist/Researcher - Mathematical Optimization, Analytics, and Statistics

SK Innovation’s System and Optimization Laboratory has immediate openings for full-time positions in mathematical and stochastic optimization, computing, analytics, statistics, and logistics and supply chain optimization. Located in Seoul, in the heart of Asia, the opportunity presents the discernable individual the opportunity to work in a dynamic environment, located in an exciting part of the world fast emerging as a leader in technology and innovation.

The successful candidate will join a growing multidisciplinary group of world class scientists performing breakthrough research and providing innovative solutions to support better decision making across all sectors of the corporation including (but not limited to) the oil and gas industry, battery and green energy, chemicals and lubricants, and I/E materials. The lab offers a unique environment that combines basic research with applied projects reflecting the need to work closely with end users of the technology while also conducting research in multidisciplinary groups, publish in leading journals, and present in scientific forums. 

The applicant should have a Ph.D. degree in Applied Mathematics, Industrial Engineering, Chemical Engineering, Operations Research, Statistics, Computing, or other related scientific discipline, with both theoretical and practical experience in their respective areas. Applicants with expertise in large-scale mixed-integer programming, global optimization and non-linear programming, statistics, data mining and forecasting, or stochastic optimization and simulation are especially encouraged.

SK Innovation offers an excellent working environment and a competitive compensation and benefits package, and is committed to the ongoing development of their staff. Interested candidates are invited to send their cover letter and CV to Faramroze Engineer at f.engineer@sk.com.

Founded in 1962 as Korea’s first oil refiner, SK Innovation, formerly SK Energy, is one of the world’s leading energy and petrochemical companies.  SK Innovation is a Fortune Global 100 company and posted US$60.7 billion in sales in 2011. Additional information about SK Innovation can be found at http://eng.skinnovation.com.

Postdoc position in optimization, U of Edinburgh, UK

Applications are invited for an 18-month fixed-term Postdoctoral Research Assistant position in Optimization (ref: 3015812) in the School of Mathematics at the University of Edinburgh, United Kingdom.

Applicants must hold a Ph.D. in Operations Research, Computer Science, Mathematics, Statistics, Machine Learning or a related field by the starting date of the appointment, which is September 1, 2012 (+- 1 month, based on mutual agreement). The successful applicant will join the Edinburgh Research Group in Optimization (http://www.maths.ed.ac.uk/ERGO/) and will work on gradient algorithms for large-scale optimization. Strong publication record and good computational skills are essential; background in gradient methods,  large-scale optimization and HPC desirable.

More information and online application forms:
https://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3015812&CFID=93551117&CFTOKEN=70587149