Data Science Internship

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Computer Science | Data Science internship: failure pattern analysis Beschrijving Introduction The Leveling department within Development and Engineering at ASML develops advanced wafer measurement solutions that support nanometer-level accuracy in semiconductor manufacturing. This internship focuses on improving the understanding of machine failures through data-driven analysis of event logs. You will help transform complex and noisy machine data into meaningful insights that support system reliability and performance. Your work will contribute to identifying recurring failure patterns and enabling more effective failure classification. This internship offers the opportunity to combine artificial intelligence, machine learning, and data mining in a highly innovative engineering environment. Your assignment During this internship, you will develop methods to analyze machine event logs and identify patterns related to machine failures. You will design and evaluate machine learning approaches that classify failure modes and uncover valuable insights from historical machine data. The results of your work may support engineers in understanding failure behaviour across multiple systems and improving future diagnostics. Your main responsibilities will be: - Develop algorithms to clean, compress, and structure noisy machine event data. - Identify recurring failure signatures using data mining and machine learning techniques. - Build and evaluate supervised and unsupervised classification models. - Validate findings using historical data and expert feedback. - Investigate similarities and differences between failure patterns across machines. - Assess the practical value of the proposed solution for engineering use cases. - Present results and recommendations to engineers and researchers. This is a master's thesis/non-thesis internship for a minimum of 6 months, 5 days per week (3 days on-site). The start date of this internship is...

计算机科学 | 数据科学实习：故障模式分析 描述 引言 ASML开发与工程部门的Leveling部门开发先进晶圆量测解决方案，支持半导体制造中的纳米级精度。本次实习聚焦于通过对事件日志的数据驱动分析，提升对机器故障的理解。你将帮助将复杂且嘈杂的机器数据转化为有意义的洞察，以支持系统可靠性和性能。你的工作将有助于识别重复出现的故障模式，并实现更有效的故障分类。本次实习提供了在高度创新的工程环境中结合人工智能、机器学习和数据挖掘的机会。 你的任务 在本次实习期间，你将开发方法来分析机器事件日志，并识别与机器故障相关的模式。你将设计和评估机器学习方法，对故障模式进行分类，并从历史机器数据中挖掘有价值的洞察。你的工作成果可能帮助工程师理解多个系统中的故障行为，并改进未来的诊断。 你的主要职责将是： - 开发算法来清理、压缩和结构化嘈杂的机器事件数据。 - 使用数据挖掘和机器学习技术识别重复出现的故障特征。 - 构建并评估有监督和无监督分类模型。 - 使用历史数据和专家反馈验证发现。 - 研究不同机器之间故障模式的相似性和差异。 - 评估所提出解决方案在工程用例中的实际价值。 - 向工程师和研究人员展示结果和建议。 这是一项硕士论文/非论文实习，最短为期6个月，每周5天（3天现场）。本次实习的开始日期是……

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