Sensor Systems Engineer - Radar & ML Innovator

Johns Hopkins University Applied Physics Laboratory LLC

Laurel (MD)

On-site

USD 85,000 - 165,000

Full time

4 days ago
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Job summary

Johns Hopkins Applied Physics Laboratory in Maryland seeks engineers and scientists to join our Sensor Systems Group, developing and testing cutting-edge radar and sensor systems. You will engage with a team of scientists and engineers working to develop high-impact solutions for the Nation's defense.

You will model radar systems, prototype signal processing algorithms, apply machine learning to large datasets, and fabricate test fixtures to support test events while ensuring system requirements

Qualifications

  • Bachelor's or Master's in a listed technical field.
  • Interest in sensor systems and knowledge in Matlab, Python, C++.
  • Strong problem solving/analytical skills.
  • Excellent verbal, written and presentation communication skills.
  • Ability to collaborate with colleagues from varied subject areas.

Responsibilities

  • Develop and analyze models of next generation radar and sensor systems
  • Prototype novel signal processing algorithms to be deployed by our military
  • Use machine learning techniques to augment current and future capabilities
  • Fabricate test fixtures to support test events
  • Apply data science and machine learning to complex, large scale datasets
  • Test radar systems to assure that they meet requirements

Skills

Problem solving
Communication
Team collaboration
Self-motivation

Education

Bachelor's degree in Electrical Engineering
Bachelor's degree in Computer Science

Tools

MATLAB
Python
C++

Job description

Johns Hopkins Applied Physics Laboratory in Maryland seeks engineers and scientists to join our Sensor Systems Group, developing and testing cutting-edge radar and sensor systems. You will engage with a team of scientists and engineers working to develop high-impact solutions for the Nation's defense.

You will model radar systems, prototype signal processing algorithms, apply machine learning to large datasets, and fabricate test fixtures to support test events while ensuring system requirements

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