CPS Internship: Build Smart Autonomous Systems

Johns Hopkins Applied Physics Lab; Laurel (MD); Sur place; USD 32 000 - 66 000; Plein temps

Résumé du poste

Johns Hopkins University Applied Physics Laboratory (APL) is seeking a Cyber-Physical Systems Intern to contribute to smart, networked prototypes that sense, process, and communicate data for homeland security and defense. You will work across multi-disciplinary teams on CPS ranging from small autonomous devices to larger smart environments.

You will gain hands-on experience testing and refining CPS with technologies from machine learning to autonomous platforms, while preparing for eventual

Qualifications

• Pursuing a Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mechanical Engineering, Robotics, or a closely related field.
• Have a minimum 3.0 GPA on a 4.0 scale.
• Proficient in software development using Python, C, and/or C++.
• Strong communication and interpersonal skills, and ability to collaborate across diverse teams.
• Eligible for Interim Secret clearance by start date and ultimately Secret clearance; U.S. citizenship required.

Responsabilités

• Contribute to hardware and/or software components of cyber-physical prototypes ranging from tiny autonomous systems to entire environments.
• Test, evaluate, troubleshoot, and document cyber-physical systems to understand strengths and weaknesses.
• Develop capabilities to ensure operational success for sponsors in real-world deployments.

Connaissances

Python

C

C++

GPA 3.0

Communication skills

Security clearance eligibility

Formation

Bachelor’s / Master’s / PhD in CS/CE/EE/Mech/Robotics

Outils

ROS

OpenCV

NumPy

Pandas

Arduino

Raspberry Pi

NVIDIA Jetson

Description du poste

Johns Hopkins University Applied Physics Laboratory (APL) is seeking a Cyber-Physical Systems Intern to contribute to smart, networked prototypes that sense, process, and communicate data for homeland security and defense. You will work across multi-disciplinary teams on CPS ranging from small autonomous devices to larger smart environments.

You will gain hands-on experience testing and refining CPS with technologies from machine learning to autonomous platforms, while preparing for eventual