Post-doctoral positions at Sandia National Labs

 The Scalable Algorithms Department at Sandia National Labs is seekingcandidates in the areas of high performance numerical linear algebra including parallel preconditioners, parallel graph algorithms, block Krylov methods, mixed-precision computations, algorithms for multicore/manycore architectures, and object-oriented scientific software engineering.

Experience in one of more of the following areas is highly desired:

-- CUDA, OpenCL, Threaded Building Block, or other advanced parallel programming paradigms.
-- Parallel implicit and explicit mesh based PDE solution approaches.
-- Parallel particle methods such as Molecular Dynamics and Direct Simulation Monte Carlo.
-- Advanced object-oriented software engineering practices and processes.
-- High-performance computing on distributed, parallel and/or other specialized architectures.
-- Scientific programming using Trilinos and /or Zoltan.
-- Algebraic multigrid.

You can find the official announcement and apply at http://preview.tinyurl.com/7mfuktl, job #639457.

Best regards,
Jonathan Hu

Senior Operations Research Scientists (Stochastic Optimization); Principal Scientists (and Sabbaticals)

Inventory Planning and Control (IPC) group at Amazon is looking for experienced operations research scientists with expertise in stochastic optimization. These positions are in Seattle, WA. Please forward this to your current/past graduate students who might be interested.

We are also looking for Principal Operations Research Scientists (8-10 years of experience) and experienced faculty members (with experience in inventory optimization, supply chain management, simulation-optimization methods etc.) who might be interested in spending their sabbatical with us or who might be interested in full-time positions.

Thanks

Deepak Bhatia
Senior Manager, IPC

 

Job Description:

Inventory Planning and Control (IPC) is looking for experienced operations research scientists with expertise in stochastic optimization and its application in inventory optimization, supply chain management, vendor selection etc.

Candidate will be responsible for developing solutions to better manage/optimize worldwide inventory, while providing the best experience to our customers at the lowest possible price. This position will focus on identifying opportunities to improve existing business processes, analyzing these opportunities and developing new strategies (and better models) to further improve the existing inventory planning/optimization and vendor selection processes/algorithms. The successful candidate will be a person who enjoys and excels at diving into data to analyze root causes and implement long term solutions – changes in business policy or processes, updates to the models in our systems, creation of effective metrics and functionality.  The goal is to improve the Amazon customer experience through improved selection, product availability, and fulfillment reliability while enhancing profitability. This high visibility role requires partnering with retail, finance and operations teams to impact our bottom-line.

Inventory Planning and Control (IPC) owns Amazon’s global inventory planning systems: we decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy.   We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large scale supply chain planning and optimization systems. IPC fosters new game-changing ideas, continuously improves, resulting in sophisticated, intelligent and self-learning models. IPC is unique in that we’re simultaneously developing the science of supply chain planning and solving some of the toughest computational challenges at Amazon.  Unlike many companies who buy existing off-the-shelf planning systems, IPC is responsible for studying, designing, and building systems to suit Amazon’s needs.  Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best research scientists and software developers in the business, shaping our roadmap to drive real impact on Amazon’s long-term profitability. If you’re interested in getting closer to the “action,” IPC is the place for you.

Basic Qualifications:

PhD degree in operations research, management science, statistics, engineering, mathematics, or computer science and 4-5 years of related work experience

    * Expertise in stochastic optimization
    * Ability to develop system prototypes
    * Experience with SQL and statistical tools (SAS, R, SPSS)
    * Experience utilizing problem solving and analytical skills

Preferred Qualifications:

    * Proficiency in one or more of programming languages JAVA, C++,
    * Expertise in one or more of the following: optimization, probability theory, queuing theory, game theory, simulation, decision analysis, stochastic models, system dynamics, forecasting and mathematical modeling.
    * A good understanding of analysis of algorithms and computational complexity is desirable.
    * Technical aptitude and familiarity with the design and use of complex logistics software systems
    * Familiarity with inventory planning, supply chain management, capacity management (forecasting, planning, optimization, and logistics) gained through work experience or through graduate level education
    * Project management experience desired for working on cross-functional projects
    * Excellent written and verbal communication skills

Amazon is an equal opportunity employer

Full Time Position in Mathematical Optimization at ExxonMobil Research and Engineering Company

ExxonMobil's Corporate Strategic Research laboratory has an immediate opening for a full-time staff position in mathematical optimization within our Complex Systems Science Section. The laboratory is located 50 miles from New York City in scenic western New Jersey.

We are looking for a creative and resourceful individual to join our research and development team to solve challenging problems in the area of large-scale mixed-integer programming. The successful candidate will join a dynamic group of scientists performing breakthrough research across all sectors of the corporation, developing new approaches to solve our most challenging optimization problems.

The applicant should have a Ph.D. degree in Applied Mathematics, Industrial Engineering, Chemical Engineering, Operations Research or other related scientific discipline, with theoretical and practical experience in mathematical programming modeling and algorithm development. Expertise in large-scale mixed-integer programming is required. Knowledge and background in methods for mixed integer nonlinear programming, global optimization or optimization under uncertainty are desirable. Candidates with strong computing skills are preferred. The applicant must have excellent communication skills and a demonstrable ease of interaction with researchers and business partners of varying backgrounds. The candidate should be willing and able to learn new skills and grow into new science areas.

ExxonMobil offers an excellent working environment and a competitive compensation and benefits package. Please submit your cover letter and resume to our website at www.exxonmobil.com/ex and apply to Mathematical Optimization.

ExxonMobil is an Equal Opportunity Employer

Ahmet B. Keha, Ph.D.

Corporate Strategic Research

ExxonMobil Research & Engineering

1545 Route 22 East

Annandale, NJ 08801

(908) 730-2803

ahmet.b.keha@exxonmobil.com

TITLE: Robust Optimization with Multiple Ranges and Chance Constraints

TITLE:                      Robust Optimization with Multiple Ranges and Chance Constraints

SPEAKER :                Ruken Duzgun, Ph.D. Candidate
                                    Department of Industrial and Systems Engineering

DATE :                        Tuesday, December 6, 2011 at 3:00 p.m.

LOCATION :             Room 453 Mohler, 200 W. Packer Avenue

ABSTRACT:  The first part of the dissertation focuses on the case when the uncertain parameters come from multiple ranges.  This arises when the uncertain parameters, such as cash flows, depend on underlying discrete random variables, such as the success level of a new project.  Using multiple ranges has the same tractability as the traditional robust optimization approach but leads to less conservative results, while providing better representation of underlying random factors.  The second part of the dissertation briefly introduces how robust optimization can be connected with chance constraints.  We show that the Bernstein approximation of robust binary optimization problems leads to robust counterparts of the same structure as the deterministic models, but with modified objective coefficients that depend on a single new parameter introduced in the approximation.
 
BIOGRAPHY:  Ruken Duzgun is currently a doctoral candidate in Industrial Engineering at Lehigh University.  She received her B.S. degree in Industrial Engineering from Middle East Technical University in Ankara, Turkey, in 2007 and her M.S. degree in Management Science from Lehigh University, in 2010.  Her research introduced new methods in robust optimization, with applications to investment planning and project selection.  She is a Rossin Doctoral Fellow since 2010, and a member of INFORMS and Women in OR/MS (WORMS) Society.