STR's Sensors Division, specifically the Systems Autonomy, Analysis, and Modeling (SAAM) Group, is looking to solve real-world problems in national security by developing, adapting, and applying cutting-edge autonomy research. The Senior Researcher will focus on innovating and applying AI/ML and other decision-making algorithms to develop autonomy capabilities, particularly in simulation, to address complex national security challenges.
Requirements
- Good programming skills in one or more scientific programming languages, such as C/C++, Python, or MATLAB; preferably in Linux.
- Some experience or interest in at least two of the following: machine learning, algorithm development, modeling & simulation, optimization, controls, signal processing, tracking, electronic warfare, resource allocation.
- Experience with mission simulators such as AFSIM or NGTS or physics-based simulators such as XPatch or Crisp
- Experience with machine learning tasks such as image recognition, deep reinforcement learning, semantic segmentation, graph neural networks, and tools such as PyTorch, Tensorboard, and Ray.
Responsibilities
- Design, develop, and modify cutting-edge machine learning and other algorithms to conduct studies and form conclusions for autonomy and other challenges
- Create, expand, upgrade, and maintain algorithms in software
- Design, develop, validate, and manage large-scale simulations and simulation tools
- Contribute to comprehensive technical reports and presentations for government customers, articulating the implications of simulation findings on national security
Other
- This position requires the ability to obtain a Security Clearance at the Secret (S) level, for which US citizenship is needed by the U.S government
- BS +5 years’ experience, MS +3 years’ experience, or PhD in Electrical Engineering, Physics, Applied Mathematics, or related field
- Strong technical writing and communication skills
- Active Secret or TS/SCI clearance
- Experience writing proposals and/or giving briefings to both highly technical and less technical audiences
