Master Thesis Internship – Intelligent Thread across Semiconductor Product Lifecycle

NXP Semiconductors NV; Eindhoven; On-site; EUR 8,900 - 13,000; Part time

Benefits offered by this job

Internship contract

Job summary

NXP Semiconductors N.V. in Eindhoven is offering a Master Thesis internship focused on transforming the Intelligent Thread into an active engineering knowledge and learning system.

You will engage in Phase 1 and Phase 2 work, analyzing how engineering knowledge travels across the product lifecycle and proposing concrete research directions. The role targets Master thesis candidates in Industrial Engineering, AI/ML, Data Science, or related fields, with programming experience in Python and an

Qualifications

• Master Thesis student or equivalent in a relevant field.
• Strong analytical and problem-solving skills.
• Excellent in English with good communication across cultures.

Responsibilities

• Define and explore NXP's Intelligent Thread as an engineering knowledge and learning system.
• Identify opportunities to strengthen PLM knowledge sharing and learning.
• Propose Phase 1 and Phase 2 research directions for the Master thesis.

Skills

Open mindset

Analytical skills

Communication skills

English proficiency

Python basics

SPARQL basics

Education

Industrial Engineering and Innovation

Artificial Intelligence and Machine Learning

Data Science or Computer Science

Operations and Logistics Management

Tools

Python

SPARQL

Job description

Our Vision

The most powerful AI use cases need smart contexts, through an Intelligent Thread.

Our Vision

The most powerful AI use cases need smart contexts, through an Intelligent Thread. A thread that connects knowledge and expertise, materialized in data, from various business functions and domains, across the full Product Lifecycle. From marketing and design, sales and operations, to delivery and service. From hundreds of ideas, thousands of designs, millions of recipes, to billions of devices. Produced in physical and digital factories. Used, out in the field, like in cars, drones, and robots. Today, knowledge and expertise across functions and domains is scattered, fragmented. Rather than viewing the Intelligent Thread as a mechanism for traceability, this project explores how it can become an active engineering knowledge and learning system. A continuously evolving foundation that provides domain specific contexts, enables deployment of AI agents, supports knowledge exchange, informed decision making, and organizational learning throughout the Product Lifecycle.

Our Team

Your will join the Product Lifecycle Solutions team, an enthusiastic group of Business focused IT professionals, working from Eindhoven, Netherlands, and Bangalore, India. Operating in the global, highly competitive, highly dynamic Semiconductor industry. Stepping up to challenges in the Product Lifecycle space, enabling NXP success, at high speed. You will work from the High Tech Campus in Eindhoven. You will be able also to catch-up with master thesis students in other areas, like Supply Chain and Quality.

Your Assignment

Phase 1

Phase 1 Focuses On Understanding NXP's Current Digital/Intelligent Thread And Identifying Opportunities To Strengthen It As An Engineering Knowledge And Learning System. Through Stakeholder Interviews, Process Analysis, And Exploration Of The Existing PLM Ecosystem, The Student Will Investigate

• How engineering knowledge and expertise flows across the product lifecycle
• Which engineering artifacts, decisions, and rationale are connected, and where important knowledge gaps exist
• Challenges in (the speed of) knowledge exchange, change management, and cross-domain collaboration
• Opportunities to improve how engineering knowledge is captured, connected, maintained, and reused throughout the product lifecycle

The outcome will be an assessment of the current Intelligent Thread from a knowledge and learning perspective, together with a prioritized set of research directions, that will form the basis for the master's thesis.

Phase 2

Building on Phase 1 findings, Phase 2 focuses to investigate one selected challenge and to design, prototype, or evaluate a concrete solution that strengthens the Intelligent Thread, as an active engineering knowledge and learning system.

Possible Research Directions Include

• Engineering knowledge representation and integration
• Continuous traceability and knowledge evolution
• Cross-domain change impact analysis
• Capturing and preserving engineering rationale
• Cross-domain engineering decision support
• Knowledge discovery and reuse across the product lifecycle

Artificial Intelligence, including agentic AI, knowledge graphs, or other intelligent technologies may be explored, where appropriate as enabling technologies. Nevertheless, the primary focus is on improving the Intelligent Thread as a living engineering knowledge and learning system, that supports faster and smarter engineering decision making, throughout the Product Lifecycle.

Your Profile

Master Thesis Student, Preferably In One The Following Areas

• Industrial Engineering and Innovation
• Artificial Intelligence and Machine Learning
• Data Science or Computer Science
• Operations and Logistics Management

Including The Following Skills

• Open, innovative and collaborative mindset
• Strong analytical and problem-solving skills
• Strong communication skills, navigating across cultures and functions
• Excellent in English language
• Python would be good to get, while SPARQL is a nice to have

Our Offering

• Conducting your master thesis research,
  • based on an internship contract
• Interesting/challenging working environment,
  • in a global operating company
  • in a highly competitive high-tech industry
  • with a wide variety of development and learning opportunities

More information about NXP in the Netherlands...