Hi there, I am Niklas.
I build stuff that learns.

I'm (almost) a Machine Learning PhD who loves working with data. My research focused on making sense of datasets and teaching algorithms to make smarter decisions.

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About Me

I love building machine-learning systems and the infrastructure around them. Everything from reinforcement-learning pipelines and large-scale simulations to the data platforms and services they run on. I worked with noisy real-world datasets, sequential decision-making problems, and environments where models influence physical systems, where robustness and edge-case handling aren't optional.

Beyond transport AI, I've also deployed full technical stacks for a rail startup: data ingestion pipelines, analytics layers, distributed scrapers, and their deployments. I enjoy the full spectrum, from training agents on millions of samples to designing the systems that keep those models reliable, observable, and scalable.

If a problem involves dynamics, data at scale, or building intelligent ML systems from scratch, that's usually where you'll find me.

Expertise

Deep Learning 90%

Python / Torch 85%

Data analytics 70%

Ops & Deployment 60%

Infrastructure 50%

Documentation & TeX 70%

Selected Work

INDUSTRY

Nox Mobility: Founding Engineer & Tech Lead

2025

Shaped the technical foundation as founding engineer for a mobility startup from zero to investor-ready. Designed and implemented full-stack architecture: containerized microservices, data pipelines, multi-database systems, web scraping infrastructure, CI/CD, and analytics dashboards. Followed the ride for 9 months from zero to seed, defining architecture, security, DevOps strategy, and product-market-fit.

Python OpenAPI Docker PSQL Airflow Bare Metal CI/CD System Architecture

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RESEARCH

2-Level Reinforcement Learning Framework

2025

A modularized DRL framework for autonomous vessel control on inland waterways, featuring separate agents for high-level path planning (considering traffic rules and dynamic obstacles) and low-level path following. Outperforms traditional APF and PID controllers by 65% in obstacle avoidance while reducing control effort.

Deep RL Path Planning COLAV Autonomous systems Big Data Real-World Validation

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RESEARCH

AIS Trajectory Extraction Framework (α-method)

2024

An open-source Python framework for extracting clean ship trajectories from noisy AIS big data. Uses maneuverability-dependent α-quantile-based filtering to handle technical inaccuracies and compliance issues in raw AIS records. Robustly extracts long, uninterrupted trajectories for maritime domain awareness and algorithm testing.

Automatic Identification System Big Data Automatic Processing FOSS

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RESEARCH

Bootstrapped DRL for Robust Path Following

2024

A bootstrapped Deep Q-Network (DQN) controller for autonomous vessel navigation on restricted inland waterways. Handles challenging conditions like high flow velocities and shallow banks on the Rhine. Trained in diverse, realistic environments and validated against real-world data, demonstrating superior adaptability compared to vessel-specific PID controllers.

Deep RL Path Follwing Autonomous systems Real-World Validation

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