Semiconductor production is one of the world’s most complex manufacturing challenges. As a Data Scientist in imec’s AI Department, you will apply advanced data science and AI to semiconductor R&D challenges. Your work will enable next-generation chip manufacturing, improve engineering decision-making and contribute to the Digital Twin roadmap. Are you ready to join our highly innovative, multidisciplinary and international research environment?
What you will do
Your mission is to become a data science expert in the semiconductor domain, helping engineers turn complex R&D data into deeper process understanding and better decisions, and creating the data foundations for next-generation AI applications.
Your main responsibilities are to:
- Analyze the semiconductor process, equipment and metrology data together with domain experts to identify key process drivers, anomalies, dependencies and improvement opportunities.
- Assess data quality, context and lineage, and account for data sparsity, measurement variability, temporal dependencies and experimental structure when building analytical datasets and models.
- Develop features and analytical representations from heterogeneous and multimodal engineering data, including sensor streams, time series, documents, images and historical experiments, for use in statistical and machine-learning models.
- Apply statistical analysis and machine learning techniques to uncover patterns, support root-cause analysis, accelerate experimentation and improve engineering decision-making.
- Organize semiconductor knowledge and engineering context for effective access by AI systems, using metadata, semantic structures, embeddings, knowledge graphs and retrieval techniques where appropriate.
- Communicate insights and recommendations to stakeholders and collaborate with data, software and AI engineers to integrate analytical solutions into operational R&D workflows.
What we do for you
We offer you the opportunity to join one of the world’s premier research centers in nanotechnology at its headquarters in Leuven, Belgium. With your talent, passion and expertise, you’ll become part of a team that makes the impossible possible. Together, we shape the technology that will determine the society of tomorrow.
We are committed to being an inclusive employer and proud of our open, multicultural, and informal working environment with ample possibilities to take initiative and show responsibility. We commit to supporting and guiding you in this process; not only with words but also with tangible actions. Through imec.academy, 'our corporate university', we actively invest in your development to further your technical and personal growth.
We are aware that your valuable contribution makes imec a top player in its field. Your energy and commitment are therefore appreciated by means of a market appropriate salary with many fringe benefits.
Who you are
You are curious, analytical and hands-on, with an interest in understanding complex scientific and engineering data and turning it into actionable knowledge. You enjoy working across semiconductor, data science and AI disciplines.
You bring:
- A master’s degree in data science, artificial intelligence, computer science, statistics, applied mathematics, physics, engineering or a related quantitative discipline
- Strong foundations in applied statistics and machine learning, with solid Python skills and experience analysing complex scientific or industrial datasets.
- The ability to independently drive analytical work from problem formulation and data exploration through modelling, validation and communication of results.
- Strong collaboration and communication skills, enabling you to work effectively with domain experts, data/software engineers and AI specialists.
Experience in one or more of the following domains is considered a strong advantage:
- Semiconductor manufacturing, process engineering, equipment and sensor data
- Metrology, inspection, defectivity, yield or materials characterization
- Scientific AI, digital twins, surrogate or hybrid modelling
- Knowledge graphs, semantic data models or retrieval-augmented AI
- Scalable or cloud-based analytical and ML environments
