Master Thesis Reliability

ZEISS; Oberkochen; On-site; EUR 13,000 - 20,000; Full time

Job summary

ZEISS invites motivated students to join a hands-on role in reliability engineering, blending academics with practical research in microelectronics tooling. You will work on modeling load-driven degradation data and validate methods through simulations.

Ideal candidates are enrolled in engineering or physics programs with strengths in data science and programming in Python or MATLAB, and an independent, solution-oriented approach to challenges.

Qualifications

• Enrolled in a degree program with excellent academic performance in engineering or physics.
• Academic focus in reliability, modeling, data analysis or product development.
• Experience in reliability engineering and programming with Python or MATLAB.
• Knowledge of data science, statistics is an advantage.

Responsibilities

• Familiarize with state-of-the-art methods in reliability modeling.
• Develop modeling approaches for load-driven degradation data.
• Implement and compare modeling approaches to identify the most efficient method.
• Validate and optimize the developed models through test runs and simulations.
• Prepare structured documentation of results and present key insights to the project team.

Skills

Python

MATLAB

Data analysis

Statistics

Reliability engineering

Communication

Education

Engineering or physics (BSc/ MSc)

Mechatronics

Tools

Python

MATLAB

Job description

Enabler for smaller, more powerful, and more energy-efficient microchips Working for tomorrow today. Around 80 percent of all microchips worldwide are produced using ZEISS technologies. As the centerpiece of every electronically controlled system, they have become an integral part of our everyday lives – whether in smartphones, smart homes or smart factories. ZEISS is a technology leader in the field of semiconductor manufacturing equipment. With high-precision lithography optics, photomask systems and process control solutions, ZEISS enables the production of ever smaller, increasingly powerful, and more energy-efficient microchips, and thus plays a pivotal role in the age of micro- and nanoelectronics.

Your Role

With us, you have the opportunity to perfectly combine your studies with practical experience while actively contributing to exciting projects. This allows you to gain valuable skills, expand your network, and grow both professionally and personally.

• Familiarize yourself with state-of-the-art methods in reliability modeling
• Develop modeling approaches for load-driven degradation data
• Implement and compare different modeling approaches to identify the most efficient method, taking statistical variation and measurement uncertainty into account
• Validate and optimize the developed models through test runs and simulations
• Prepare structured documentation of results and present key insights to the project team

Your Profile

• Enrolled in a degree program with excellent academic performance in mechanical engineering, electrical engineering, physics, mechatronics or a comparable field
• Academic focus in technical reliability, modeling, data analysis / data science, or product development
• Initial experience in reliability engineering and programming with Python or MATLAB
• Basic knowledge of data science, modeling, and statistics is an advantage
• Independent, solution-oriented and communicative working style

Sounds exciting? Then become part of #teamZEISS and help us shape the future!