PhD Position - AI-assisted model-based design flow for AMS/RF circuits (m/f/d)

NXP Semiconductors N.V.; Deutschland; Vor Ort; EUR 30.000 - 40.000; Teilzeit

Zusammenfassung

A leading semiconductor company is seeking a self-motivated PhD student to develop AI frameworks for SysMLv2 models. Responsibilities include collaborating on requirements, ensuring model validation, and participating in publications. The role requires a Master's degree in a relevant field and offers an 80% part-time commitment over three years.

Qualifikationen

• Enrollment in a university PhD program and approval required.
• 80% part-time commitment for a 3-year period.

Aufgaben

• Design an AI-assisted framework for automatic translation into SysMLv2 models.
• Collaborate to break down system requirements to IP requirements.
• Develop methodology for validating SysMLv2 model correctness.

Kenntnisse

AI background

Programming in Python

NLP techniques

Problem-solving skills

Good communication skills

Ausbildung

Master's degree in electrical engineering, computer science, or related field

Jobbeschreibung

Overview:

We are seeking a self-motivated PhD student to join the NXP research team responsible for developing methodologies and flow solutions to enable high sigma analysis of complex AMS/RF systems. This PhD position will be part of the new project with the goal to enable a top-down model-based design flow for AMS/RF circuits using SysMLv2. SysMLv2 is a powerful language to create requirements, behavioral and architectural models of complex systems. The proposed PhD topic aims to investigate how state-of-the-art NLP techniques and LLMs can be used to automate creation of SysMLv2 models from natural language descriptions.

As NXP is the world's No 1 supplier of automotive semiconductors, you will have an opportunity to be part of developing high quality solutions to address future customer requirements.

Your tasks:

• Design and implement an AI-assisted framework for the automatic translation of natural language descriptions into SysMLv2 models for AMS/RF circuits
• Collaborate with System architects to break down system requirements to IP requirements
• Develop a methodology for validation of the generated SysMLv2 models with respect to semantic correctness and model consistency
• Collaborate with other PhD candidates in the team on bringing AMS/RF characterization data into SysMLv2 models to check their consistency with requirements
• Create demonstrator of an AMS/RF system modeling environment
• Participate in the dissemination and exploitation of the results (scientific publications, presentations at national and international conferences)

Education and requirements:

• Master's degree in electrical engineering, computer science, system engineering, or a related field, with a good AI background
• Good programming skills in programming languages such as Python
• Good understanding of NLP techniques such as tokenization and named entity recognition
• Basic understanding of AMS/RF systems
• Familiarity with LLMs and Generative AI
• Basic SysMLv2 knowledge is a plus
• Interest in statistics is a plus
• Good problem-solving skills with the ability to research independently
• Good communication and presentation skills
• Fluent in spoken and written English

Eligibility criteria:

• University PhD program enrollment and approval: This position requires registration as a PhD student at Institute Division of Design of Cyber-Physical Systems, Department Computer Science, RPTU Kaiserslautern. The application is subject to university approval. More information about enrolment requirements can be found here.
• 80 %-time commitment within the 3-year period: This position is part time (80%) limited to 3 years.

Application procedure:

The application must be submitted digitally and must include:

• A motivation letter (1-2 pages explaining why you would be a good fit for this role)
• CV
• Contact information of at least one referee (preferably two)
• MSc diploma incl. grade transcripts
• Optionally, you could add personal projects on the AI-related topics publicly available (e.g. GitHub projects), scientific publications you have (co)authored, recommendation letters, etc.

Please note that this is a contract in part-time (80%), limited to 3 years.

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