Physics-Informed ML for Device Modeling Intern

About GlobalFoundries
GlobalFoundries is a world-leading contract manufacturer for the global semiconductor industry with facilities in Dresden, Singapore, New York and Vermont (USA). Our products are used in various technical applications, e.g. mobile communications, consumer electronics, automotive and more. GlobalFoundries employs around 13,000 people worldwide, including 300 in Sofia. 

Our Sofia based team is enhancing GF’s scale and capabilities, while strengthening competitiveness of its specialized application solutions to further position the company for growth and value creation. 

Our Design and Technology Enablement teams are working on the development of a broad portfolio of semiconductor technologies ranging from 350 nm down to 12nm including FD-SOI, RF, High-Voltage and automotive applications. 

Internship Program Overview:

Our Interns & Co-ops are our entry-level talent pipeline for GF across the globe. Our goal is to provide students with a meaningful work experience that will equip them with the skills to embark on a career in the fast-paced and growing semiconductor industry after graduation. As an intern at GF, you’ll experience one-on-one mentorship, work assignments that prioritize your growth and potential, professional development opportunities, and the chance to network with executives.

Summary of Role:

This position focuses on advanced modeling and platform development, combining device physics, circuit-level analysis, and machine learning. The Co-Op will work on:

• Establishing the relationship between device-level behaviour and circuit-level performance, and

• Developing automation and software tools (including GUI-based local platforms) to support model QA and model workflow execution.

This role is ideal for students interested in physics-informed ML, data-driven modeling, and practical software tools for semiconductor modeling workflows.

Essential Responsibilities include:

• Under the guidance of senior modeling and research engineers, work on device- and circuit-level modeling research, with a focus on closing gaps between simulation and silicon.

• Analyze device and circuit-level measurement data to understand sensitivities and performance impact of device parameters.

• Develop methodologies to use circuit-level measurements to guide device-level parameter tuning and model calibration.

• Apply machine learning and neural network techniques to modeling, parameter optimization, and data analysis tasks.

• Explore inverse modeling and optimization approaches, including reinforcement learning (RL)–based methods, for automated parameter adjustment.

• Design and develop software tools or a local platform with a GUI to integrate internal modeling resources and support model QA workflows.

• Enable users to configure, run, and monitor existing model workflows through automated or GUI-based interfaces.

Other Responsibilities:

• Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.

Required Qualifications:

• Education – At least a sophomore at time of application and actively pursuing a Master’s, or PhD in Device Physics & Electrical Engineering or related field through an accredited degree program during the time of internship.

• Must have at least an overall 3.0 GPA and be in good academic standing.

• Language Fluency - English (Written & Verbal)

Preferred Qualifications:

• Experience working with EDA tool vendors and circuit‑level simulation environments.

• Exposure to compact modeling, parameter extraction, or model QA workflows.

• Background in automation, software tool development, or GUI‑based applications.

• Familiarity with machine learning, data analysis, and deep learning techniques, with particular interest or experience in reinforcement learning (RL).

• Prior related internship or co-op experience.

• Strong written and verbal communication skills.

• Strong planning, organizational, and problem-solving skills.

We Offer:

• 20 h/week, aligned with university schedule

• Monthly Scholarship

• 1 - 1.5 years duration

• Top Office Location