"PhD on combined physics- and machine learning-based modeling of complex dynamical systems"

Job Summary: This PhD position involves modeling complex dynamical systems by combining physics-based knowledge and machine learning to create models with superior predictive capacity. The role focuses on applying these techniques in high-tech domains like semiconductor machines, working within a team of 2 PhD students at Eindhoven University of Technology.

Responsibilities: Develop novel tools for hybrid modeling to generate highly predictive dynamical models. Apply and valorize scientific results in semiconductor equipment for heterogeneous integration, collaborating with industry partners like ASMPT.

Qualifications and Requirements: A master’s degree in dynamical systems, mechanical engineering, electrical engineering, AI and machine learning, or applied mathematics. Strong background in dynamical systems, systems theory, and machine learning. Affinity with mechanical engineering applications such as mechatronics or robotics. Knowledge of programming languages like Matlab or Python. Experience or interest in dynamical systems and machine learning. Ability to work independently and in a team, collaborate with industry and academics, and fluency in English.

What the Employer Offers: A 4-year fixed-term contract with full-time employment. Salary range of €3,059 to €3,881 per month. Benefits include pension scheme, paid pregnancy and maternity leave, partially paid parental leave, year-end bonus of 8.3%, annual vacation pay of 8%, high-quality training programs, excellent technical infrastructure, on-campus facilities, commuting allowance, and support for international candidates including the 30% tax facility. You will spend 10-15% of your time on teaching tasks and have access to graduate courses and collaboration opportunities.