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@@ -325,8 +325,7 @@ <h3>Research Fellow</h3>
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 	   					<h4><a href="https://www.lmu.de/en/" target="_blank">Maximilian University of Munich</a></h4>
 
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-							I was awarded a fellowship from the Data Science for Social Good Foundation<a href="https://www.datascienceforsocialgood.org/" target="_blank"> (DSSG)</a>, through which I collaborated with Munich's Machine Learning Center <a href="https://mcml.ai/" target="_blank"> (MCML)</a>, <a href="https://www.en.statistik.uni-muenchen.de/index.html" target="_blank">LMU's </a>, statistics department and <a href="https://www.bmi.bund.de/EN/topics/civil-protection/civil-protection-node.html" target="_blank">Bavaria's civil protection.</a>
+							I was awarded a fellowship from the Data Science for Social Good Foundation<a href="https://www.datascienceforsocialgood.org/" target="_blank"> (DSSG)</a>, during which I collaborated with Munich's Machine Learning Center <a href="https://mcml.ai/" target="_blank"> (MCML)</a>, the statistical department of <a href="https://www.en.statistik.uni-muenchen.de/index.html" target="_blank">LMU</a>, and Bavaria's state Ministry of the Interior <a href="https://www.bmi.bund.de/EN/topics/civil-protection/civil-protection-node.html" target="_blank">(BMI)</a>.
 							Our collaboration aimed to develop a web application for identifying optimal water extraction points for firefighting purposes. The project involved integrating statistical mapping, machine learning techniques, and interactive maps. 
 							Additionally, we implemented routing algorithms, containerized the application using Docker, deployed it on AWS, and presented our findings to Germany's federal parliament.
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