Senior Research Scientist - Network Systems

Peraton•Basking Ridge, NJ

1d•$146,000 - $234,000

About The Position

Peraton Labs is seeking a Senior Research Scientist to contribute to research and development addressing complex challenges for government agencies, utilities, and commercial customers. Peraton Labs' cybersecurity and network research protects mission-critical systems and national cyber infrastructure, while improving efficient use of spectrum and system resources through computer network defense, secure-by-design approaches, AI/ML, and deterministic and stochastic optimization. The work may span networking and wireless research, including 5G and non-terrestrial networks; cybersecurity research involving formal methods, side-channel analysis, and high-fidelity emulation of cyber-physical systems; resilient networking for cyber missions; software-defined networking for cyber defense and deception; and artificial intelligence, including reasoning, probabilistic inference, and machine learning. The role requires expertise spanning RF communications and signal processing, machine learning and data applications, and programming. The successful candidate will apply these skills to research and implementation, communicate technical results to stakeholders, and advance prototypes from concept through demonstration.

Requirements

Strong foundation in RF communications theory, including modulation and demodulation, propagation, link budgets, and spectrum analysis.
Hands-on digital signal processing experience, including filtering, FFT and spectral analysis, detection, estimation, and adaptive algorithms.
Demonstrated expertise in compressive sensing, including sparse signal recovery, L1/L2 optimization, sensing matrix design, and sub-Nyquist sampling techniques.
Proficiency designing and implementing supervised, unsupervised, deep-learning, and reinforcement-learning algorithms, with an emphasis on signal and time-series data.
Experience building end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring.
Knowledge of CNN, RNN/LSTM, transformer, autoencoder, and GAN architectures, with applied experience choosing models for RF/signal and data-analytic problems.
Strong data analytics skills, including exploratory analysis, statistical modeling, visualization, and deriving actionable insight from large or complex datasets.
Hands-on experience with PyTorch, TensorFlow/Keras, scikit-learn, and data tools such as NumPy, pandas, and Spark.
Experience with MLOps practices, including version control, Docker containerization, CI/CD, experiment tracking such as MLflow or W&B, and model serving.
Strong programming skills in Python and C/C++; proficiency with Git, Linux development environments, and scripting.
A bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a related discipline, with at least 12 years of experience; alternatively, at least 10 years with an MS/MA or at least 7 years with a Ph.D.
Demonstrated project or publication record spanning both RF/signal processing and machine-learning domains.
U.S. citizenship.
Clear technical communication skills, including the ability to explain algorithms, implementation, and results to stakeholders and write technical papers and reports.
Self-directed approach and experience taking prototypes from concept through demonstration.

Nice To Haves

Experience applying machine learning to RF problems such as signal classification, spectrum sensing, RFI mitigation, cognitive radio, direction finding, or emitter identification.
Background in DoD/IC programs, electronic warfare, SIGINT, or radar systems.
Active security clearance or ability to obtain one.
MATLAB experience.

Responsibilities

Conduct research involving networking and wireless systems, potentially including 5G and non-terrestrial networks.
Contribute to cybersecurity research, including formal methods, side-channel analysis, and high-fidelity emulation of cyber-physical systems.
Develop or contribute to resilient networking for cyber mission goals.
Work on software-defined networking for cyber defense and deception.
Apply artificial intelligence approaches, including reasoning, probabilistic inference, and machine learning.
Design and implement machine-learning algorithms and end-to-end ML pipelines for signal and time-series data applications.
Analyze large or complex datasets and derive actionable insights.
Translate algorithms into implementations and communicate findings to stakeholders; write technical papers and reports.
Take prototypes from concept through demonstration.