<div class="content-intro"><div><span style="font-size: 18pt;"><strong>About Neo4j:</strong></span></div> <div> <p>Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter.</p> <p>Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business.</p> </div> <div><span style="font-size: 18pt;"><strong>Our Vision: </strong></span></div> <div> <p><span style="font-weight: 400;">At Neo4j, we have always strived to help the world make sense of data. </span></p> <p><span style="font-weight: 400;">As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive.</span></p> </div></div><p><span style="font-size: 18pt;"><strong>The Role:</strong></span></p> <div class="p-rich_text_section" style="text-align: left;">Are you at the end of your studies and want to immerse yourself in graph technology? We are now looking for students who want to do their Master’s Thesis alongside us at Neo4j!<br>As part of Neo4j engineering in Malmö, you will work with a diverse team of talented colleagues worldwide. You will receive advice and continuous support from us - we are experts in graph technology and positioned to help you perform to the best of your ability.</div> <div class="p-rich_text_section" style="text-align: left;"> </div> <div class="p-rich_text_section" style="text-align: left;"> <div class="p-rich_text_section" style="text-align: left;"><strong data-stringify-type="bold">Past Thesis Topics:</strong></div> <div class="p-rich_text_section" style="text-align: left;"> </div> <div class="p-rich_text_section" style="text-align: left;"><strong data-stringify-type="bold"><strong data-renderer-mark="true">Multi-Stage Dashboard Generation over Graph Databases with LLMs</strong></strong></div> <div class="p-rich_text_section" style="text-align: left;"> <p>This thesis investigates natural language–driven dashboard generation over graph databases, with a focus on maintaining alignment between user intent and generated outputs. While prior work in natural language to visualization has improved single-chart generation, producing coherent multi-view dashboards remains challenging, particularly for graph-structured data where relationships and query constraints are central. To address this, the work frames dashboard generation as a multi-stage workflow rather than a single-step task. A pipeline is designed that separates intent interpretation, schema grounding, planning, query generation, and validation, with selective human-in-the-loop clarification. The proposed approach is implemented for a Neo4j database and evaluated against a baseline generation method through a comparative dashboard study and an interactive workflow study. Results suggest that the proposed workflow improves perceived dashboard quality and user experience, particularly in coherence, usability, and intent alignment. The findings indicate that structuring generation as an inspectable and iterative process is a promising direction for dashboard generation.</p> </div> <div class="p-rich_text_section" style="text-align: left;"><strong>Force Directed Drawing Algorithms and Parameter Optimisation:</strong><br>Through my thesis I have implemented and compared some different graph drawing algorithms in addition to some methods to speed up the slow parts of these algorithms. These algorithms were then used to test what to the best of my knowledge is a novel approach to select parameter values for graph drawing algorithms. For this, I use methods similar to those used in Machine Learning to select parameter values and measure the utility of any set of parameters by creating my own utility function. I created this function by looking at objective measures of drawing quality that are commonly known, such as the number of edge crossings, along with the time it took to draw a given graph. The resulting method for parameter optimisation could find significant increases in the speed of graph drawing for several of my implemented drawing algorithms without compromising drawing quality. Furthermore, the approach is not specific to any parameter set, and can with some modification be applied to any graph drawing algorithm dependent on some constants.<br><strong data-stringify-type="bold"><br></strong><strong>Modeling Profiling Data in a Graph Database for Performance Analysis:</strong><br>Benchmarking is an important part of the development process for any mission-critical application. By inspecting profiling data, developers can identify bottlenecks and performance regressions before they reach the customers. </div> <div class="p-rich_text_section" style="text-alig