Daniel Haas
Summary
I have a Master's degree in Computational Sciences and a Bachelor's in Physics with an emphasis in Mathematics. I now work as an AI engineer, where I apply AI solutions to 3D metal printing and develop the supporting software. My expertise is within Data Science, Data Engineering, and Back-End development.
Education
UiO (University of Oslo)
M.SC. IN COMPUTATIONAL SCIENCE: PHYSICSThesis: Deep Learning Methods for Quantum Many-body Systems
UFMG (Federal University of Minas Gerais)
B.SC. IN PHYSICSWith complementary specialization in Mathematics
Skills
Data Science
Data Analysis and Modeling, Computer Vision, Time-series analysis, Neural Networks (PyTorch, TensorFlow)
Data Engineering
ETL, Database Modeling, SQLAlchemy, Alembic, Apache Airflow
Development
FastAPI, Flask, HTML5, CSS, CI/CD, TDD
Programming
Python (JAX), C++/C (OpenMP, MPI), JavaScript
General
Git, AWS, LaTeX
Experience
3D Components AS
AI ENGINEER- Leading the development of AI-driven solutions for metal 3D printing applications, with a focus on enhancing process efficiency and precision
- Designed and implemented both front-end and back-end architecture for a desktop application tailored to additive manufacturing needs
- Developed and currently maintain a vast material database to support advanced machine learning models and optimize manufacturing workflows
- Created a machine learning pipeline that enables users to train and deploy ML models to identify optimal process parameters for welding and additive manufacturing
Kristiania University College
MACHINE LEARNING INTERNRefining and publishing the development of a machine learning pipeline for automatic structure annotation on cardiac MRI images, with state-of-the-art architectures and topology-preserving loss functions for medical structures.
University of Oslo
Teaching Assistant for Computational Physics II: Quantum Mechanical Systems
- Remodeling the course from C++ to Python with JAX, grading projects, and providing help during sessions
Teaching Assistant for Applied Data Analysis and Machine Learning
- Assisted in flipped classroom sessions, graded projects, and supported students
Simula
Developed a novel machine learning pipeline for automatic structure identification and annotation on cardiac MRI images.
Improved lecture material for a course on Programming with Scientific Applications.
Inmetrics
DATA SCIENTIST- Implemented machine learning models for time-series forecasting and anomaly detection. The models significantly reduced downtimes, improved predictability in IT service capacity planning, and accelerated the generation of weekly reports by over 400%
- Modelled and maintained data pipelines, databases, and ETL processes
- Deployed Flask applications to visualize machine learning model outputs in a no-code interface, utilized daily by over 20 companies