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We are searching for an applied computational mathematician/ engineer/ scholar to leader next-generation, complex, stochastic predictive simulations in a business practical real world scenario.
The position demands utilizing expertise in 1 or many of the of the following i.e. Applied mathematics, Statistics and computational science with the objective of enabling uncertainty quantification and measurement at an extreme scale in a real world scenario across the following industries
o Metals & Mining
o Public Sector
o Consumer Products
o Process Industries
o Financial Institutions
o Hardware & Software
o Social Welfare
o Health Care
o Consumer Products
o Media and Networks
o Oil & Gas
o Utilities & Alternative Energy
o Medical Devices & Technology
o Travel & Tourism
o Airlines & Transportation
o Financial Services
o Industrial Services
The individual would be required to demonstrate scenario planning and visualize possibilities in a practical scenario setting
He should be able to communicate complex terminologies in a practical setting to a business audience
Fundamental responsibilities included
a. Participating in the development of Uncertainty Quantification & Measurement methodologies necessary for informed fact based decision-making
b. Design and execution of scalable numerical methods for Uncertainty Quantification and Measurement
c. Collaboration with experts in the above industry in 1 or many of the following area Statistics, Operations Research, Computer Engineering, Computer Sciences, Economics, Bio-Statistics, Bio-Informatics in the above industries
d. Design of experiments and building models with an expertise in the design and implementation of numerical algorithms and methods in one or more high-level computing languages within a team environment
Specialization in 1 or many of the following:
1. Model reduction
2. High-performance computing
3. PDE-constrained optimization
4. Computational fluid dynamics
5. Computational structural mechanics
6. Markovian Decision Processes
The individual should have applied 1 or many of the following in a practical setting/ scenario. Whilst applying /interviewing for the following position the individual would be required to talk and demonstrate how he or she has applied or will apply the following
- Stochastic collocation and Galerkin methods
- Multilevel and reduced order methods
- Linear and Eigenvalue solvers
- Hierarchical and low-discrepancy sampling
- High-dimensional interpolation and integration
o The minimum required education is a BS/MS/ Ph.D. in applied mathematics, computational science or related field.
o Strong background in stochastic differential equations, partial differential equations, numerical analysis, methods for uncertainty quantification and high-performance computing.
o Excellent soft skills
o Tested written and oral communication skills
o Extensive expertise in numerical analysis of stochastic PDEs
o Experience in the development of large-scale numerical algorithms and simulation codes
Applicants should have received their latest degree not more than 5 years before making the application. All degree requirements must be completed before the starting of appointment.
We are necessarily not interested in individuals having all of the traits outlined above and below in as much that he/she has a willingness to acquire capabilities and has expertise in some of the areas. We will work with the specific individual to help him/her succeed.
For obvious reasons preference would be given to individuals having demonstrated knowledge and expertise in many of the following.
The position requires collaboration within a multi-disciplinary research environment consisting of mathematicians, computational scientists, computer scientists, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratorys missions. Specific responsibilities include participating in the development of novel UQ methodologies necessary to facilitate science-informed decision-making, design and implementation of scalable numerical methods for UQ, collaboration with experts from various scientific disciplines on UQ and applications, and following team planning, goals and quality processes.
The minimum required education is a Ph.D. in applied mathematics, computational science or related field. Strong background in stochastic differential equations, partial differential equations, numerical analysis, methods for uncertainty quantification and high-performance computing. Expertise in more than one area of particular relevance to simulations of interest, such as:
Stochastic collocation and Galerkin methods
Multilevel and reduced order methods
Linear and eigenvalue solvers
Hierarchical and low-discrepancy sampling
High-dimensional interpolation and integration
Design of experiments Demonstrated experience in the design and implementation of numerical algorithms in one or more high-level computing languages within a team environment
Effective interpersonal skills.
Demonstrated written and oral communication skills.
Candidates must have a proven publication track record.
Experience working in a multi-disciplinary research environment. Extensive expertise in numerical analysis of stochastic PDEs Experience in the development of large-scale numerical algorithms and simulation codes.Additional Information:Applicants cannot have received the most recent degree more than five years prior to the date of application and must complete all degree requirements before starting their appointment.
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