Signal Processing Intern

Job Description Summary

GE Vernova Advanced Research has one opening for a Signal Processing Intern. The selected intern would have an opportunity to work on cutting edge inspection technologies alongside World Class Researchers. The internship duration can be for a duration of 3-11 months.

Job Description

GE Vernova Advanced Research has one opening for a Signal Processing Intern. The selected intern would have an opportunity to work on cutting edge inspection technologies alongside World Class Researchers. The internship duration can be for a duration of 3-11 months.

The focus of the role is to learn and contribute to key cutting-edge NDE technologies for Industrial Inspections for all of GE Vernova’s products. The selected intern would be working on

Data collection, preprocessing, developing AI models (e.g., neural networks), and optimizing code in Python or MATLAB for Non-destructive evaluation (NDE)

Data Handling: Collect, clean, and annotate RF sensor, or image datasets, often mixing signals to create training sets.

Algorithm Development: simulate digital signal processing (DSP) algorithms, including filtering, Fourier and wavelet analysis.

AI/ML Integration: Develop and train Machine Learning or Deep Learning models (e.g., CNNs, RNNs) for signal classification, denoising, or feature extraction.

Implementation & Testing: optimize code in C/C++, Python, CUDA, or MATLAB.

Required Qualifications & Skills

Academics: The candidate should be currently pursuing a Bachelor’s/ Master’s/Doctoral degree in Electrical Engineering, Computer Science, Physics, or related fields.

Programming: Strong skills in Python (withlibraries like NumPy, PyTorch, TensorFlow) and/or MATLAB.

Technical Knowledge: Foundational understanding of digital signal processing (sampling, filtering, transforms) and machine learning principles.

• Demonstrated ability to quickly learn new, unfamiliar product knowledge, technology areas
  
  
• Excellent oral and written communication skills
  
  
• Collaboration: Work with interdisciplinary teams to test systems, analyze performance and document findings.