Doctor of Science,
Russian Academy of Sciences,
Moscow, Russia
Data Correcting and Tolerance Based Algorithms
Data Correcting and Tolerance Based Algorithms representing a unified approach to modeling and solving problems in Applied Combinatorial Optimization, e.g. Preemptive Single Machine Scheduling, Maximization (Minimization) of Submodular (Supermodular) Functions, Pseudo-Boolean Polynomials in Multidimensional Big Data Aggregation, Max-Clique, Max-Cut (including Quadratic Cost Partition), Capacitated Vehicle Routing in Cloud Computations applied to Virtual and Physical Resources, Facility Locations, Cell Formation in Industrial Engineering some of which might be found on amazon.com click
Advanced courses in Mathematical Programming, Discrete (Combinatorial) Optimization, Algorithms and Data Structures, Mathematical Statistics and Standard Software
Ability to design and implement algorithms including the proof of their correctness based on advanced data structures.
At least 3 years experience in C++, MatLab, CPLEX or similar software
Shortlisted candidate will be invited for a 30-min skype interview and one week for implementation an algorithm with reported computational study
The aim of this program is to establish world wide competitive Mathematical Models, Algorithms, and Software with the purpose to solve computationally intractable benchmark instances