Large Language Models & Knowledge Representation Researcher

Introduction ASML lithography systems are essential in the production of advanced memory and logic devices. Designing these complex systems necessitates extensive knowledge, and expert input is required to diagnose any performance degradation or failure (from design engineers, field service experts, etc.). However, this diagnosis is typically carried out by generalists (field engineers). If your ambition is to apply your knowledge, personal leadership skills to very complex challenges and you feel excited to develop new approaches to take devices already at the edge of technology to the next level, please read further. ASML Research is investigating methods to address the described gap by utilizing techniques to gather expert knowledge from various sources such as physics, documents, and software. This information will be used to develop solutions to support field engineers and design new generation systems. Are you challenged by demanding research projects in a multidisciplinary context? Do you have the hands-on technical capabilities to provide innovative solutions? Does contributing to the world's most advanced lithography solutions make you feel responsible and proud? Then this might be the right opportunity for you. Job Mission We are seeking a Scientist with expertise in Large Language Models and experience in knowledge representation and reasoning strategies, who is interested in developing new systems, demonstrators, and working within a team environment. You will be working with graph databases, ontologies and large language models, to help define and execute research in the domain of knowledge representation and applications, with a strong focus on supporting the system-diagnostic processes. Information will be extracted from various multi-modal sources (text, graphics, structured information sources etc). The research will take place in collaboration with Academic Institutes, ASML D&E and Customer Support departments. Education and Experience - PhD / Post-Doctoral researcher in Mathematics, Artificial Intelligence/Machine Learning or logic. - M.Sc. in for instance Data Science, Mathematics, Computer Science, Statistics - 4+ years experience in applied R&D in the field of NLP / LLM, reasoning or related fields. - 4+ years of experience in applying this type of technology, preferably in a high-tech context or environment. (Experience working with tools and programming languages ​​related to knowledge representation, reasoning and its applications). - Ability to communicate complex technical and mathematical concepts in a clear and precise manner. - Strong affinity in machine-learning and statistical methodologies. Skills & Responsibilities Working at the cutting edge of tech, you'll always have new challenges and new problems to solve - and working together is the only way to do that. You will be part of a creative, dynamic work environment where you will collaborate with supportive colleagues from various departments. There is always space for creative and unique points of view. You'll have the flexibility and trust to choose how best to tackle tasks and solve problems. To thrive in this job, you'll need the following skills: - A hands-on, solution-oriented, innovative mind-set - Ability to work, communicate and influence across functional teams from adjacent domains - Experience with defining and monitoring research projects on academic institutes - You are self-propelling and can define the research direction. - High personal ambition and drive, commitment to team and company - Good & proactive communication in an international and multidisciplinary environment - Pragmatic approach and pro-active attitude, with result focus and a 'can do' spirit Context of the position You will be a member of the Diagnostics and Data-Science Research team. You will interact with your peer data scientists and experts from other disciplines, and build relationships with relevant academic research institutes. Other information Research, Artificial Intelligence, Large Language Models, Graph Theory, Graph Databases, Knowledge Representation and Elicitation, Formal Logic, (Bayesian) reasoning systems, Natural Language Processing, Statistics, Machine Learning, Data Science, Semiconductors, Lithography.

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