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Research topic: The role of digital twin in production and demand planning
Building Feature Graphs from Logistics Data for Causal Analysis
Ontology-Guided Hybrid Causal Discovery in ESG Data: LLM-Based Development of Query Interface
Comparative Analysis of Statistical, Machine Learning, and Deep Learning Imputation Algorithms for Time Series Data: Foundations for Reliable Causal Discovery
Research topic: Explainable AI for Object Detection in 3D PointClouds
Combining XGBoost, Causal AI and Generative AI for Explainable Oncology under the EU AI Act
LLMs for Extracting Key ESG Indicators from Public Reports / Retrieval-Augmented Verification Framework for Faithful KPI Extraction
Linking Supply Chain Resilience & Sustainability KPI Ontologies to Causal Graphs and Comparing Them with Data-Driven Causal Discovery
LLM-Assisted Collection of ESG Data from News Articles and Media Sources
Identifying essential ESG key performance Indicators of the automotive industry: An Industry based survey using MCDM methods
Automated Root Cause Analysis in Automotive Software testing Leveraging LLMs
Developing a Conceptual Framework for Using Large Language Models (LLMs) to Make the ESG Reporting Process More Sustainable: A Systematic Literature Review
Research topic: Object detection from 3D images of digital twin assets for transport infrastructure management using explainable deep learning approach
Research topic: Deep reinforcement learning for Industrial task scheduling: PPO algorithm approach
Research topic: What are the benefits and challenges of using federated learning for ESG assessments in supply chains?
Plant Disease Detection using Explainable Deep Learning Integrating with LLMs
Comparing How LLMs Differ in Extracting Causal Relationships from Natural Language Text through ESG Reports