Resilient RF Machine Learning for Dynamic and Novel Emitter Environments

ORAU; San Antonio (TX); On-site; USD 90,000 - 120,000; Full time

Job summary

ORAU in San Antonio is seeking a U.S. citizen for a position developing artificial intelligence (AI) and machine learning (ML) for radio frequency (RF) sensing. The ideal candidate will help create AI/ML algorithms and will participate in data collection and field tests.

Qualifications include a Doctoral Degree and experience in programming languages like LabView, MATLAB, and Python. The role may involve publishing research in peer-reviewed journals.

Qualifications

• Experience in software programming such as LabView, MATLAB, and Python.
• Familiarity with self-supervised learning and foundational AI models.
• Experience deploying and evaluating AI systems in real-world scenarios.

Responsibilities

• Develop customized, lightweight, scalable AI/ML algorithms for RF applications.
• Participate in data collection and field tests as needed.
• Publish findings in a peer-reviewed journal.

Skills

Software programming (LabView, MATLAB, Python)

Familiarity with foundational models

Real-world deployment of AI systems

Working with large streaming datasets

Embedded edge devices experience

Education

Doctoral Degree

Master's Degree

Bachelor's Degree

Job description

Description

The Army Research Laboratory (ARL) in San Antonio is seeking a U.S. citizen interested in developing artificial intelligence (AI) and machine learning (ML) for radio frequency (RF) sensing. The ideal candidate will be part of the team that develops new paradigms and technical approaches based on AI/ML that enable learning, adapting, and performing automated detection, classification, modulation recognition, and application intent of complex RF emitters in Multi‑Domain Operations (MDO) environments. The candidate will conduct research that seeks to improve the AI/ML solutions for RF applications.

Responsibilities

• Real‑world data‑driven development of customized, lightweight, scalable AI/ML algorithms for RF applications.
• Internship will primarily involve algorithm development but may include participation in data collection and field tests.
• Publish a paper in a peer‑reviewed journal.

Qualifications

• Experience in software programming (LabView, MATLAB, Python).
• Familiarity with foundational models and self‑supervised learning.
• Experience in real‑world deployment and evaluation of AI systems.
• Expertise in working with large streaming datasets.
• Understanding of computer system design and decentralized systems.
• Hands‑on experience with embedded edge devices, including the deployment of AI algorithms to edge devices.

Eligibility Requirements

• Citizenship: U.S. Citizen Only.
• Degree: Doctoral Degree required.
• Academic Level(s):
  1. Doctoral Degree – Postdoctoral Fellow.
  2. Doctoral Degree – Senior Fellow (5+ years).
  3. Master’s Degree – Journeyman Fellow.
  4. Master’s Degree – Senior Fellow (7+ years).
  5. Bachelor’s Degree – Journeyman Fellow.
• Discipline(s):
  • Computer, Information, and Data Sciences
    • Artificial Intelligence (including Robotics, Computer Vision, Human Language Processing, and Machine Learning)
    • Computer Architecture and Grids
    • Computer Science - Languages and Systems
    • Computer Science - Theoretical Foundations
    • Computer Science (general)
    • Computer Systems Analysis
    • Computer Systems Design (including Signal Processing)
    • Data Science
    • Databases, Information Retrieval, and Web Search
    • Graphics and Visualization
    • Human Computer Interaction
    • Information Science and Technology
    • Information Security and Assurance
    • Networks and Communications
    • Operating Systems and Middleware
    • Scientific Computing and Informatics
    • Software Engineering
  • Engineering
    • Mathematics and Statistics
    • Physics
    • Science & Engineering‑related

Contact

Point of Contact: ARL‑RAP. Email: [e-mail].