Sensors Directorate Internship Program

Participants address layered sensing modalities, artificial intelligence, aerospace components and sensor components, multispectral exploitation and detection, spectrum warfare and high performance computing to enhance Air Force capabilities. The program typically employs more than 60 interns from the top U.S. research universities — like the University of Dayton — to work with accomplished mentors and researchers in a well-equipped scholarly environment.

The Sensors Directorate mission focuses on affordable sensor and countermeasure technologies for reconnaissance, surveillance, precision engagement and electronic warfare systems. Core technology areas include radio frequency and electro-optical sensing, electromagnetic spectrum warfare, multi-domain sensing autonomy, resilient mission systems and enabling devices and components.

• Multi-Domain Autonomous Sensing
• Aerospace Components and Subsystems Technology
• Multispectral Sensing and Detection
• Spectrum Warfare
• Modeling and Simulation
• Enabling Sensor Devices and Components
• High Performance Computing

• Joint Measured and Synthetic Data Training for CNN-Based SAR Target Classifier
• Deep Learning Model-based Algorithm for SAR ATR
• High-Performance Multi-Agent Based CNN Systems for SAR Feature Extraction
• Toward morGAN: Modern Object Recognition using GANs
• Siamese and Triplet Networks for Measuring Quality of Synthetic SAR Data
• One Shot Learning using Siamese Neural Networks
• GANs for EO Illumination
• Generative Adversarial Networks to Close SAR Synthetic/Measurement Gap
• BicycleGAN for Improving Synthetic SAR Data
• Convolutional Sparse Representations of 3D LADAR Data for Robust Classification
• Sparsity-Based SAR ATR: A Preliminary Study
• Sparsity-based Interferometric SAR
• Back Projection to 3D Probabilistic Electro Optic Model
• SAR ATR Performance Validation of Synthetic and Measured data
• Score Estimation of Klander Data using Boosting Algorithms
• Articulating CAD Models
• Generating a Hybrid and Fused Scale Model, Measured, and Synthetic Data Set
• Target Recognition of CAD Model Fidelity
• Material Characterization in the Microwave Spectrum
• Model-in-the-Loop Waveform Optimization for Amplifiers
• Cognitive Radar Algorithms for Waveform Parameter Adaptation using Software-Defined Radar
• Multi-Waveform STAP with Embedded Communications
• Narrowband Imaging of Rotating Objects
• Using LMS to Process Passive Noise Radar Data
• Cramer-Rao Bounds for Direction Finding
• Statistical Based Frequency Hopping Pattern Detection
• Neural Net Feature Space Enhancement for Open Set Classification
• Reinforcement Learning and Generalization in Unknown Environments
• Simulation of Detection with Distributed Infrasonic Sensors
• Bistatic SAR Integration and Experimentation in EW Simulation
• Small UAV Vulnerability Study
• Goal-Driven Cognitive Algorithmic Processing for EW
• Environment-Robust LWIR Hyperspectral Change Detection
• Hyperspectral Image Creation using GANs
• SensorCraft
• Robust Recognition for 3D LADAR
• Extracting Anomolous Patterns from Movement Data
• Spectral Clustering for Directed Graphs
• Ledoit-Wolf covariance estimation for STAP
• Machine Learning Models for Transfer Learning Applications
• Manifold Transfer Learning and Deep Network Fine-Tuning for EO Satellite Images
• Capsules vs. ConvNets
• Temporal Event Detection
• Multi-Task Semantic Video Captioning in the Wild and Attacks Against Model-based Text Transcription
• Ground Truthing and Exploiting WAMI
• GAN-based Superresolution of Aircraft Satellite Images
• Signal Modeling and Optimal Partitioning
• Gaussian Mixture Models for DeepNav
• DeepFilter: Integrating sequential outputs of deep networks
• Scalability of DeepNav
• Capsule Networks, Deepnav
• Improvements for SLAM Benchmarking
• Feasibility Study of Eye Gaze Assisted EO Truthing
• Practical particle filter sampling using an observation estimate
• α-KS Goodness of Fit for Interacting Multiple Model Testing
• Conservative PDFs and Data Fusion Applications
• Verifying the Uncertainty of PoseNet Outputs
• Simultaneous Localization and Mapping in Mobile Robots
• Multisensor Fusion Data Collection System
• Automatic Covariance Calibration of Uncharacterized Sensor for Multisensor/Multitarget Tracking and Fusion
• Model-Assisted Multi-Target Tracking with Simultaneous Sensor Calibration
• Learning and Evaluating Deep Features for Data Association
• Contextual Refinement for Semantic Image Segmentation
• Probabilistic Reasoning for Decision and Control of UAVs
• Probabilistic Reasoning with SNNs and the NEF
• Biological Systems and Their Application in Robotics
• Complex Algorithm Assessment on Reconfigurable Devices
• Integration of ROS with the TurtleBot 2
• Detecting and Analyzing Anomalies with Topological Data Analysis
• Comparing Multiple Synthetic Predictors
• Efficient J2K Image Compression for Low SWaP Devices
• Real Time Kinematic GNSS Receiver Network
• Spatial Heterodyne Imaging systems

• Full-time appointments for the 14-week summer program duration (early May to early August).
• Competitive pay ranges from $751 to $1,124 per week for undergraduate students and $1,171 to $1,357 per week for graduate students.
• Program-provided campus housing for all interns upon request.
• Mentor-driven collaborative intern research.
• Interns have access to high-performance computing resources, outstanding measurement and modeling tools, and mission-relevant data.
• Work with 60 interns from top U.S. universities guided by 40 expert researchers.
• Program culture includes organized tours, focused seminars, short courses, workshops, use of university facilities, community campus housing and available extra-curricular activities.
• Students with strong academic records enrolled in (or are soon to be enrolled into) an undergraduate or graduate degree program in engineering, computer science, math, physics, or related fields are encouraged to apply.
• U.S. citizenship and a minimum of 18 years of age are strongly preferred.

Note: This is a full-time, nominally 40 hour/week appointment. The job classification lists "part-time" to address the partial-year (14-week) duration.