Postdoc on Data Management, Process Science, and Neuro-Symbolic AI for Predictive and Prescriptive Process Analytics

This postdoctoral position focuses on advanced expertise in data management, process science, and neuro-symbolic artificial intelligence, specifically applied to predictive and prescriptive process analytics. Candidates should possess a strong foundation in managing complex data systems, including data integration, quality assurance, and scalable storage solutions. Expertise in process science is essential, encompassing process modeling, analysis, and optimization techniques to understand and improve organizational workflows. A deep understanding of neuro-symbolic AI approaches is critical, combining neural network methodologies with symbolic reasoning to enhance interpretability and decision-making in AI systems. The role requires proficiency in developing predictive models that forecast process outcomes and prescriptive analytics that recommend optimal actions. Familiarity with machine learning frameworks, AI toolkits, and data analytics platforms is implied, alongside skills in algorithm development and validation. The position demands the ability to conduct interdisciplinary research at the intersection of data science, AI, and process engineering, contributing to innovative solutions for complex process challenges. Strong analytical, programming, and communication skills are necessary to collaborate effectively and disseminate findings. While specific experience and education levels are not provided, the role targets candidates with a doctoral degree and research experience in relevant fields such as computer science, data science, AI, or industrial engineering. Overall, the ideal candidate will demonstrate a combination of theoretical knowledge and practical skills in data-driven process analytics and neuro-symbolic AI techniques to advance predictive and prescriptive capabilities in process management.