The Data Science Chair at JMU Würzburg as a member of the Center for AI and Data Science (CAIDAS) offers two positions for doctoral researchers (m/w/d) in the area of machine learning and natural language processing.
The first position will work within the BigData@Geo2 project, the followup of the successful BigData@Geo project, that provides machine-learning-aided decision support for agricultural measures in the light of regional climate change. This includes prediction of crop yields and enabling proactive agricultural strategies.
The second position is part of the LitBERT project, which focuses on developing machine learning solutions to support and improve the modeling and analysis of characters in literary texts.
The BigData@Geo2 position will allow you to work on data from many small companies in the form of historical yearbooks, as well as general information from local newspapers or social media discussing local climate events. Using this data, you will develop new methods for discovering climate, ecosystem and agriculturally relevant events that assist in the overarching goal of BigData@Geo2 of assessing the economic viability of agricultural decisions, such as which crops to grow in future seasons, or predicting crop yield.
In the LitBERT position, you will work with a large collection of German literary texts such as narratives and novels to develop improved language models that provide a foundation for comprehensive analysis of literary characters. This includes automatic extraction of character traits, categorizing their types, and analysis of the complex evolution of relationships between characters in literary texts. The work focuses on using and improving state-of-the-art language models to effectively address the unique challenges posed by literary texts.
Payment is at the level of E13 according to the German federal wage agreement scheme (TV-L). Candidates are expected to have a strong background in computer science and mathematics, with a specialization in machine learning and interest in the topic of one of the positions. Prior knowledge in the field of deep learning in one of the subject areas is advantageous.
Please send your application (letter of motivation, curriculum vitae, academic records) at your earliest convenience, but no later than November 30th, 2023, to Prof. Dr. Andreas Hotho (email@example.com). You are welcome to contact us on the same address for additional details.