PhD Student

Job Title:
PhD Student - Inverse problem based on reinforcement learning

Introduction:
We drive technology innovations that shape the way we live and connect. Our technology drives the Era of Pervasive Intelligence, where smart tech and AI are seamlessly woven into daily life. From self-driving cars and health-monitoring smartwatches to renewable energy systems that efficiently distribute clean power, Synopsys creates high-performance silicon chips that help build a healthier, safer, and more sustainable world.

Experience within Synopsys:
At Synopsys, students dive into real-world projects, gaining hands-on experience while collaborating with our passionate teams worldwide—and having fun in the process You'll have the freedom to share your ideas, unleash your creativity, and explore your interests. This is your opportunity to bring your solutions to life and work with cutting-edge technology that shapes not only the future of innovation but also your own career path. Join us and start shaping your future today

Mission Statement:
Our mission is to fuel today's innovations and spark tomorrow's creativity. Together, we embrace a growth mindset, empower one another, and collaborate to achieve our shared goals. Every day, we live by our values of Integrity, Excellence, Leadership, and Passion, fostering an inclusive culture where everyone can thrive—both at work and beyond.

What You'll Be Doing:

• Build or adapt a physics-based simulator of the cold-spray process.
• Validate simulation models against experimental measurements to ensure accuracy and reliability.
• Translate target part geometries into optimization constraints for spray-gun trajectory, including position, orientation, and speed.
• Implement cost functions that capture shape error, material usage, energy consumption, and mechanical constraints.
• Develop and apply classic optimization methods and advanced reinforcement learning algorithms.
• Design neural-network policies and value functions for high-dimensional robot-arm control problems.
• Define rigorous evaluation protocols, including shape-accuracy metrics, computation time, and robustness to uncertainties.
• Compare algorithmic performance using high-fidelity simulations and, where possible, conduct tests on a physical test-bench.
• Prepare monthly technical reports, draft conference/journal papers, and present results in seminars.

What You'll Need
.

• A master of engineering
• Advanced proficiency in Mathematics and strong programming skills in Python and C/C++.
• Solid foundation in machine learning, especially optimization methods such as gradient-based solvers, evolutionary strategies, and constrained optimization techniques.
• Intermediate knowledge of reinforcement learning algorithms.
• Intellectual curiosity and willingness to explore novel modeling and learning approaches.
• Proven analytical rigor and meticulous approach to model validation and interpretation of results.
• Self-direction and initiative to drive research projects from conception through publication.
• Excellent communication skills to explain complex technical concepts to multidisciplinary teams and external audiences.
• Strong team spirit and ability to collaborate.
• Resilience and adaptability to learn from failed experiments and iterate quickly
• Effective time-management skills to meet project and thesis deadlines.
• Commitment to research ethics, reproducibility, data transparency, and laboratory safety standards.

Key Program Facts:

• Program Length: 3-year PhD program.
• Location: Mines de Paris – attached to Lyon office, France.
• Working Model: Remote.
• Full-Time/Part-Time: Full-time.
• Start Date: ASAP.

Equal Opportunity Statement:
Synopsys is committed to creating an inclusive workplace and is an equal opportunity employer. We welcome all qualified applicants to apply, regardless of age, color, family or medical leave, gender identity or expression, marital status, disability, race and ethnicity, religion, sexual orientation, or any other characteristic protected by local laws. If you need assistance or a reasonable accommodation during the application process, please reach out to us.