Your Team
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You will join the mmWave Competence Center, a group dedicated to architecting, designing, developing state-of-the-art mmWave transceivers and enabling next-Gen automotive Radarβ. Β You will be supported by mentors with experience in both circuit design and AI/ML.
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Your Responsibilities
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- Analyze the PPD circuit and identify additional measurable metrics (beyond current parameters) that influence swing accuracy.
- Design and propose methods to extract or measure these metrics in silicon or simulation.
- Develop and validate machine learning models (e.g., neural networks) to improve swing estimation accuracy, using simulation and/or measurement data.
- Benchmark your approach against existing calibration methods and document improvements.
- Prepare thorough technical documentation and present your findings to both technical and non-technical audiences.
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Whatβs in it for you
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- A unique opportunity to work on a real-world, high-impact project at the frontier of AI and analog/RF IC design.
- Develop both your technical and soft skills in a multinational, diverse environment.
- Gain experience with state-of-the-art tools and methodologies in both circuit simulation and machine learning.
- Possibility to become part of NXPβs Young Professional Talent Pool
- Working on real assignments which contribute to NXPβs objectives
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Your Profile
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- Master of Science - last year of engineer school, machine learning domain with analog IC design knowledge or affinity with analog design.
- Strong interest and coursework in both analog/RF circuit design and AI/ML (e.g., neural networks, data-driven modeling).
- Familiarity with data analysis, machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch)
- Experience with circuit simulation tools (e.g., Cadence) and programming languages (e.g., Python, MATLAB).
- Good level of English is required (attendance to meetings / presentations with international team, reading and writing of documentation)
- Proactive, analytical, and eager to learn.
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Duration Β
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This is a full-time internship (40 hours per week) with a duration of 6 months or longer. The assignment could also be suitable as a thesis/graduation project. Please note that in order to be considered for an internship/working student assignment, you need to be registered as a student during the entire period.
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