Update thesis.md

Author: bplank

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@@ -242,7 +242,7 @@ This theme covers applied NLP methods for impactful real-world domains (e.g., cl
 
 ### **Thesis projects**
 
-- *NLP for Computational Job Market Analysis.* Job postings and career paths are a rich resource to understand the dynamics of the labor market. For example, to track how  skills demands change, how career paths are affected and how educational demands may shift. Such changes have large social implications, as they can inform strategic long-term decisions of governments to react to changing structural demands in the labor force. Recently, the emerging line of work on computational job market analysis (also known as NLP for human resources) has started to provide data resources and models for automatic job market analysis, such as the identification and extraction of skills in job postings, or the prediction of career paths. For students interested in real-world applications, this theme provides multiple thesis projects for two application domains: i) understanding skills in job postings (e.g. cross-lingual or cross-domain skill and knowledge extraction to data sources like job postings, patents or scientific articles) See references of [MultiSkill](https://mainlp.github.io/projects/#multiskill) project. See also [Bhola et al., 2020](https://aclanthology.org/2020.coling-main.513.pdf), [Gnehm et al. 2021](https://www.zora.uzh.ch/id/eprint/230653/1/2022.nlpcss_1.2.pdf), our own [ESCOXLM-R](https://aclanthology.org/2023.acl-long.662.pdf) model  or ii) career path prediction, which is the task of predicting a person's next occupation based on their resume. See the [Karrierewege](https://arxiv.org/pdf/2412.14612) paper. 
+- :hourglass_flowing_sand:  *NLP for Computational Job Market Analysis.* Job postings and career paths are a rich resource to understand the dynamics of the labor market. For example, to track how  skills demands change, how career paths are affected and how educational demands may shift. Such changes have large social implications, as they can inform strategic long-term decisions of governments to react to changing structural demands in the labor force. Recently, the emerging line of work on computational job market analysis (also known as NLP for human resources) has started to provide data resources and models for automatic job market analysis, such as the identification and extraction of skills in job postings, or the prediction of career paths. For students interested in real-world applications, this theme provides multiple thesis projects for two application domains: i) understanding skills in job postings (e.g. cross-lingual or cross-domain skill and knowledge extraction to data sources like job postings, patents or scientific articles) See references of [MultiSkill](https://mainlp.github.io/projects/#multiskill) project. See also [Bhola et al., 2020](https://aclanthology.org/2020.coling-main.513.pdf), [Gnehm et al. 2021](https://www.zora.uzh.ch/id/eprint/230653/1/2022.nlpcss_1.2.pdf), our own [ESCOXLM-R](https://aclanthology.org/2023.acl-long.662.pdf) model  or ii) career path prediction, which is the task of predicting a person's next occupation based on their resume. See the [Karrierewege](https://arxiv.org/pdf/2412.14612) paper. 
 **Level: BSc or MSc.**
 
 - *Climate Change Insights through NLP.* Climate change is a pressing global issue that is receiving more and more attention. It is influencing regulations and decision-making in various parts of society such as politics, agriculture, business, and it is discussed extensively on social media. For students interested in real-world societal applications, this project aims to contribute insights on the discussion surrounding climate change. Example projects: Analyzing social media data. The data will have to be collected (potentially from existing sources), cleaned, and analyzed using NLP techniques to examine various aspects or features of interest such as stance, sentiment, the extraction of key players, etc. **References:** [Luo et al., 2020](https://aclanthology.org/2020.findings-emnlp.296v2.pdf), [Stede & Patz, 2021](https://aclanthology.org/2021.nlp4posimpact-1.2.pdf), [Vaid et al., 2022](https://aclanthology.org/2022.acl-srw.35.pdf).