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| Social Media Comments Network (Intern/Project/Thesis possible) | | Social Media Comments Network (Intern/Project/Thesis possible) | ||
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics. | | In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics. | ||
| Python | | Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| The life-circle of vanished scientific journals (Intern/Project/Thesis possible) | | The life-circle of vanished scientific journals (Intern/Project/Thesis possible) | ||
| In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies. | | In this topic, students will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies. | ||
| Python | | Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| Traffic prediction with GNN (Intern/Project/Thesis possible) | | Traffic prediction with GNN (Intern/Project/Thesis possible) | ||
| In this topic, students will study how to use XGNN to predict traffic volumn. | | In this topic, students will study how to use XGNN to predict traffic volumn. | ||
| | | Python(Modeling and Visualization). Graph and XAI knowledge. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible) | | ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible) | ||
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing. | | In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing. | ||
| | | Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus. | ||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
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| Yes | |||
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| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible) | |||
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications. | |||
| Python & CV knowledge. | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | ||
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