Master Thesis on Multimodal Large Language Models

• Building Comprehensive Knowledge in Generative AI Systems: In-depth understanding of modern neural networks, GPT models, and Transformer architectures, including training techniques, prompt engineering, and RAG (Retrieval-Augmented Generation) approaches.
• AI Tools and Technologies: Proficiency in Python, as well as specialized AI software such as FAISS, LangChain, and Pinecone for semantic search and vector databases.
• Practical Implementation of AI Applications: Development and optimization of RAG-based applications through hands-on projects and case study analysis—covering embeddings, fine-tuning, and deployment.

• In-depth insight into the management of network security and concepts/processes in order to establish, monitor, check and continuously improve information security in a company (structure of ISMS, risk analysis, security strategies, ISO standards of the ISO 2700x family)
• Focus on the subject areas of application security (secure communication, certificates, signatures, encryption), system and network security (firewalls, malware protection and intrusion detection), crisis/process/incident management and preventive security
• Practical part (hacking lab) for replicating attack scenarios and developing countermeasures

• Fundamentals of probability theory as well as descriptive and inferential statistics and their applications (with the use of R & Python), with which the most important statistical methods can be applied to solve real-world problems and to identify higher-level relationships (model building, regression and time series analysis, explorative data analysis).
• Using data visualization methods to recognize and convincingly convey the meaning of data (visual representations, animations and interactive graphics, evaluation of visualizations)

• Concepts of Machine Learning Technologies & Methodologies (Information Retrieval, Natural Language
Processing, Recommender Systems)
• Classification of data (Naive Bayesfilter e.g. Spam-Filter, Decision Trees, Random Forests, Support Vector
Machines, Neural Networks)
• Feature engineering and preprocessing of data

• Concepts of Big Data Technologies & Methodologies (such as Apache Hadoop, Apache Kafka, Apache
Cassandra, Apache Solr, Apache Spark), NoSQL, Document Stores, Object Stores
• Setup and Configuration of a small Big Data Cluster (Hadoop/YARN, Cassandra, Spark, Zeppelin) on a local
MacBook with Parallels and 5 virtualized Ubuntu Server machines.
• Implementation of a real-time data stream from the Twitter API v2 with pyspark into a mongoDB to assess the
tweet behaviour during governmental, environmental, or social crises.

• Computer Science, Statistics, Programming, Mathematics, Medical Terminologies and Classifications, Project
  Management, Ethics, Anatomy and Pathology, Hospital processes and patient pathways
• Achievements:
  • “Prix d’excellence” for the best degree “Silver” in 2015
  • “Prix d’excellence” for the best concept in the bachelor thesis in supply chain management (medication)