New opportunity

PhD IPP - R&D Digital Twin and AI application

STMicroelectronics · Singapore

About this role

Role Purpose: You will contribute to the development of AI methods for digital twin verification and reliability, combining Agentic AI and surrogate modeling. You will help create solutions that improve the automation, correctness, and efficiency of digital twin development and analysis. Responsibilities: You will work on AI-assisted code adaptation and transformation for digital twin systems, formal verification using programming languages, Lean, and related methods, surrogate modeling for faster reliability assessment and verification, and digital twin modelling, validation, and decision-support methods. Required Qualifications: Doctorate degree in AI, Software Engineering, Applied Mathematics, or similar. Strong interest in AI, formal methods, modeling, and reliability. Good programming and analytical skills. Familiarity with agentic AI, machine learning, or digital twin systems. Preferred Qualifications: Experience with formal verification, Lean, static analysis, or surrogate modeling is a plus. Company Culture: STMicroelectronics is a global semiconductor company committed to innovation, sustainability, and diversity. They are a 2026 Global Top Employer and foster an inclusive workplace that values equity in career development and opportunities.

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Skills for this role

Agentic AISurrogate modelingDigital twin systemsFormal verificationLeanMachine learningStatic analysisProgrammingAnalytical skills

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Development of AI-Driven Digital Twin Verification
The core objective is to advance methods for verifying digital twin systems. You must demonstrate how to integrate Agentic AI and surrogate modeling to enhance the automation, correctness, and efficiency of these systems, moving beyond standard simulation techniques.
Formal Verification and Code Transformation
A significant portion of the role involves applying formal methods, including Lean, to ensure the reliability of digital twin code. You will be expected to implement AI-assisted code adaptation and transformation, requiring a strong grasp of both formal logic and software engineering principles.
Surrogate Modeling for Reliability Assessment
You must prioritize the creation of surrogate models to accelerate reliability assessment and verification processes. This requires balancing computational speed with the high-fidelity accuracy necessary for semiconductor-related digital twin applications.

Likely challenges to think through

Bridging Formal Methods and Agentic AI
Integrating rigid formal verification techniques (like Lean) with the probabilistic nature of Agentic AI presents a significant technical challenge. Prepare to discuss how you maintain system correctness when using non-deterministic AI agents for code transformation.
Scalability of Digital Twin Validation
Digital twins for complex semiconductor systems can be computationally expensive. The challenge lies in developing surrogate models that are sufficiently lightweight for real-time decision support without sacrificing the reliability metrics required for industrial-grade validation.

Practice your answers

Technical practice

Agentic AI in Digital Twin Systems

What to coverExplain how you would design an agentic AI framework to automate the verification of a digital twin. Focus on the trade-offs between autonomous decision-making and human-in-the-loop validation for reliability.

Surrogate Modeling for Reliability

What to coverDescribe the process of building a surrogate model to replace computationally expensive simulations in a digital twin. Discuss how you ensure the surrogate maintains sufficient accuracy for reliability assessment.

Formal Verification with Lean

What to coverDiscuss the application of formal methods, specifically using Lean, to verify the correctness of code transformations in a digital twin. Address the challenges of scaling formal proofs to complex system models.

AI-Assisted Code Adaptation

What to coverHow would you implement AI methods to automate code adaptation for digital twin systems? Suggest approaches for ensuring that automated transformations preserve the functional integrity of the original model.

Digital Twin Validation Strategies

What to coverWhat methodologies would you employ to validate a digital twin against physical system data? Explain how you balance model fidelity with the need for real-time decision support.

Static Analysis in Reliability

What to coverExplain the role of static analysis in improving the reliability of digital twin software. Suggest how static analysis tools can be integrated into an automated CI/CD pipeline for model development.

Machine Learning for Decision Support

What to coverHow can machine learning models be integrated into a digital twin to provide actionable decision support? Discuss the importance of interpretability in these models when used for critical system analysis.

Analytical Skills in R&D

What to coverDescribe a scenario where you had to decompose a complex system problem into smaller, solvable components. Focus on your analytical process for selecting the right modeling technique for the task.

Behavioural practice

Collaboration in Diverse Environments

What to coverDescribe a time you worked in a highly diverse, cross-functional team. How did you ensure your technical contributions were understood and respected by colleagues from different professional backgrounds?

Driving Innovation and Disruption

What to coverProvide an example of a project where you challenged the status quo to improve a process or technology. What was the situation, the action you took, and the final impact on the project's efficiency?

Handling Technical Ambiguity

What to coverTell me about a research or development challenge where the requirements were unclear. How did you define your scope and maintain progress toward a reliable solution?

Commitment to Continuous Improvement

What to coverShare an example of how you have applied a continuous improvement mindset to your own work or a team process. What specific metrics or outcomes demonstrated the success of your approach?

Sharpen your knowledge

Topics to prepare

Formal Verification Methods
Review the application of formal methods to software and system correctness. Practice defining system properties and invariants, specifically focusing on how these can be applied to digital twin models to ensure reliability. This is directly relevant to the role's requirement for formal verification using Lean or similar languages.
Surrogate Modeling for Reliability
Study the conceptual framework of surrogate modeling as a technique to approximate complex simulation models. Focus on how these models reduce computational overhead for reliability assessment in digital twin systems. This aligns with the job's focus on improving the efficiency of digital twin analysis.

Skills to revise

Agentic AI Implementation
Develop a small-scale project using an agentic framework to automate a code transformation or adaptation task. This demonstrates practical familiarity with the role's requirement for AI-assisted code adaptation in digital twin systems.
Lean Theorem Prover
Complete introductory exercises in the Lean programming language to understand its syntax and application in formal verification. This is a specific preferred qualification mentioned for the role and will help in demonstrating technical readiness for formal verification tasks.
Static Analysis Techniques
Practice applying static analysis tools to identify potential reliability issues or code inefficiencies in a codebase. This skill is explicitly listed as a plus and is highly relevant to the role's goal of improving the correctness and efficiency of digital twin development.

Your preparation checklist

Synthesize your research on Agentic AI and Digital Twins
Prepare a brief summary of how Agentic AI can be applied to the automation of digital twin verification. Be ready to discuss the intersection of surrogate modeling and reliability assessment in a semiconductor context.
Review formal verification and Lean methodology
Refresh your knowledge on formal verification techniques and the Lean programming language. Prepare to explain how these methods can improve the correctness and efficiency of complex system modeling.
Prepare a technical project walkthrough
Select a past research project or academic work that demonstrates your proficiency in AI, machine learning, or static analysis. Focus on the problem-solving process, the tools used, and how you validated your results.
Formulate questions regarding R&D priorities
Develop thoughtful questions about the current challenges STMicroelectronics faces in digital twin reliability, the specific scale of the systems being modeled, and how the team balances innovation with production-grade correctness.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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