Key Skills

Job Description

Objective The goal of this internship is to investigate and develop quantum-computing-based approaches for mesh partitioning and graph optimization in plasma simulations. The work will focus on leveraging quantum annealing and hybrid quantum-classical algorithms to improve the efficiency of meshing and domain decomposition techniques used in large-scale plasma modeling. The internship will contribute to an ongoing ASML/TNO collaboration exploring how emerging quantum computing technologies can accelerate computational workflows relevant to plasma physics and semiconductor manufacturing applications. Background Plasma simulations play an important role in understanding physical processes relevant to advanced semiconductor manufacturing, including plasma-material interactions, charged particle transport, and contamination control. Modern plasma models often rely on large computational meshes whose generation, partitioning, and optimization can become computationally expensive as simulation complexity increases. Recent advances in quantum computing, and particularly quantum annealing, offer novel approaches for solving graph-based optimization problems. Many meshing and domain decomposition challenges can be formulated as graph partitioning problems and translated into Quadratic Unconstrained Binary Optimization (QUBO) models suitable for execution on quantum annealers. However, practical application requires efficient embedding of graph problems onto real quantum hardware and careful integration with classical computational methods. This internship will investigate how quantum-assisted optimization can be applied to mesh partitioning for plasma simulations and evaluate potential benefits and limitations of current quantum annealing technologies. Scope & Deliverables Study meshing and graph partitioning methods commonly used in plasma simulations. Investigate quantum annealing and QUBO-based formulations for mesh partitioning problems. Develop and implement graph-based meshing or domain decomposition workflows suitable for hybrid quantum-classical optimization. Evaluate embedding strategies for mapping graph partitioning problems onto quantum annealing hardware. Benchmark quantum-assisted approaches against classical meshing and partitioning techniques. Collaborate with researchers from ASML and TNO on algorithm development and validation. Document findings, present results to project stakeholders, and provide recommendations for future research directions. Period & Duration Start date: As soon as possible End date: 31 December 2026 Duration: Approximately 3 to 4 months (depending on start date) Location: TNO Delft & ASML Veldhoven This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R. 730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology. Inclusion and diversity ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that inclusion and diversity is a driving force in the success of our company. Need to know more about applying for a job at ASML? Read our frequently asked questions.

Core Responsibilities

Investigate and develop quantum-annealing and hybrid quantum-classical approaches to mesh partitioning and graph optimization for plasma simulations. Implement and benchmark graph-based workflows, assess embedding strategies for quantum hardware, and share findings and recommendations with ASML and TNO stakeholders.

Requirements

The posting does not specify formal education, prior experience, or other candidate requirements. The internship work involves studying meshing and graph partitioning, formulating QUBO models, implementing hybrid optimization workflows, and evaluating quantum-assisted methods against classical techniques.