AI/ML for RF sensing Postdoctoral Fellow

The candidate will conduct research that seeks to improve the AI/ML solutions for RF applications. Duties include the following: - Real-world data driven development of customized, lightweight, scalable algorithms AI/ML for RF applications. - Develop and implement a real-time online unsupervised domain adaptation (OUDA) framework for AI/ML Edge accelerators. - Internship will primarily involve algorithm development, but may include participation in data collections and field tests. - Publish a paper in a peer-reviewed journal. Candidate Qualifications: - Experience in software programming (LabView, MATLAB, Python) - Deep understanding of deep learning models - 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. AEOP Reference Code: ARLS001 To apply for this position: 1) Click Apply Now 2) Create a New Account 3) Start “2025 Fellowship Application” 4) Under “4. Fellowship Opportunities,” search for the opportunity using the AEOP reference code 5) Select to apply