This Data Science position requires deep knowledge and experience in dynamic resource management problems and concomitant algorithms or neural networks to solve Navy-relevant NP-hard problems, uncertainty analysis, and the development and testing of software for METOC-informed decision guidance with the possibility for closed-loop control for certain Navy systems (e.g., unmanned , helicopters); in particular, focusing on dynamic task and resource management and machine learning techniques to facilitate and integrate high value environmental, intelligence, and adversary behavior information into efficient, scalable, and parallelizable automated or mixed-initiative decision support system algorithms.
The candidate will be responsible for leading, planning, conducting, and documenting research on algorithm development for a portfolio of sophisticated and novel meteorology/oceanography (METOC)-based mission planning decision aids. The candidate will be responsible for solving dynamic task and resource management, mission planning, etc., multi-objective optimization problems using, but not limited to, the following: statistical analysis, estimation, linear programming, combinatorial optimization, dynamic and approximate dynamic programming, nonlinear programming, machine learning. He/She will also conduct optimization-based research on single and multi-core and multi-processor systems to investigate algorithmic scalability, sensitivity and information valuation analyses, robustness and resilience, and parallelization capabilities through use of, but not limited to, shared memory, multi-threading, multi-processing, message passing, and hybrid parallel programming models. Responsibilities will include:
a) assisting with software engineering, development, and testing of new algorithms and tactical decision aids for course of action (COA) guidance;
b) development and testing of uncertainty management approaches in a dynamic environment for robust and resilient task scheduling or resource management optimization; and
c) leveraging multi-processor and GPU compute capabilities for valuable and rapid COA guidance.
The candidate will interpret the evolving net-centric warfare guidelines as they specifically apply to METOC and METOC-impacted systems. The candidate will collaborate with organizations from the Naval and National Intelligence Communities to utilize METOC fused with Intelligence information to develop multiple efficient algorithms to suggest optimal or near-optimal courses of action to be used within decision support tools to support deployed Navy, Joint, and Coalition operating forces.
They will develop algorithms and neural networks that utilize all available environmental data (e.g., real-time satellite data, real-time radar data, unmanned sensor data, conventional meteorological and oceanographic observations, model environmental re-analyses, model environmental reforecasts, real time global environmental predictions (from multiple models, both deterministic and ensemble), real time regional environmental predictions (from multiple models, both deterministic and ensemble), etc.) along with relevant real time intelligence and behavioral information, and develops innovative optimization methods for proactive decision guidance systems. The candidate will transition and/or facilitate the transitions of advanced algorithms and optimization approaches to Navy operational systems.
Advanced degree in Computer Science, Statistics, Operations Research, or related disciplines.
- Demonstrable extensive knowledge and experience in formulating complex problems and developing and testing of efficient algorithms to tackle NP-hard problems with complex problem space. Incumbent must have experience in integrating METOC statistical models, including grids of observations and environmental model outputs, into algorithms; in particular focusing on the outputting course of action guidance for human decision makers.
- Deep knowledge of the principles, methods, and state-of-the-art techniques used in data science, operations research, and decision support, including statistical inference, correlative analysis, machine learning, dynamic programming and approximate dynamic programming. Skill in developing efficient and robust computer codes for Navy-relevant problems.
- Software Engineering: In-depth knowledge of Python, MATLAB, and FORTRAN languages. Web Programming: Incumbent must demonstrate ability to program and work in the UNIX and LINUX operating system environments. Parallel Computing: Incumbent must demonstrate knowledge of parallelizable algorithms, scalability practices, and GPU/MPI parallelization frameworks.
- Ability to communicate effectively both orally and in writing, with experience in writing theses, scientific journal articles, or manuals. Ability to write successfully funded proposals for naval concepts and ideas.
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