Combining natural language processing with reasoning in knowledge graphs: Using large language models for text-mining in domain specific (industrial) settings to construct and refine knowledge graphs. This includes building and training machine learning models (pytorch, pytorch-lightning, scipy, wandb, ...), natural language processing (spaCy, nltk, ...), and designing tools for domain experts to work with the model predictions (vue3/nuxt, tailwind, fastAPI, ...).

Courses: Advanced Programming and Algorithms & Data Structures. Applying higher programming concepts in C and Python: Logical, functional and imperative language paradigms. Understanding, designing and implementing algorithms and data structures. Assess their runtime complexity.

Enterprise content management systems.

Setting up and maintaining the technical infrastructure for the media computer science major. This included provisioning of all virtualized and bare-metal servers (Debian) using Salt and continuous monitoring using Grafana & Icinga. Virtualized servers were administrated using Proxmox. Switched lab computers to UEFI boot (both dual-boot and triple-boot of Linux, Windows, and OSX). Introduced a Windows domain controller and switched all lab PCs to Windows 10 (from 7).

Backend Software Architecture. Cleaning a HA RIAK cluster system using Elixir (Erlang). Wrote and implemented my Bachelor's Thesis: A store and forward messaging daemon which communicates with RabbitMQ brokers. Written in C heavily utilizing czmq (https://github.com/kantholtz/samwise).