System Properties — Daniel Haas

HAAS-OSL-99

Daniel Haas

AI ENGINEER · PHYSICIST · SOFTWARE BUILDER

I build applied machine-learning systems that connect models to real industrial processes, especially robotic welding and metal additive manufacturing.

Location: Oslo, Norway
Current role: AI Engineer, 3D Components AS
Current focus: Industrial AI, process optimisation, MLOps
Availability: Employed · open to collaboration

What I work on

Industrial AI: target-driven parameter selection, uncertainty-aware optimisation, digital qualification, and model deployment for robotic manufacturing.

Scientific machine learning: neural quantum states, numerical physics, medical imaging, and computationally efficient modelling.

Product engineering: translating research prototypes into usable desktop and web software with robust APIs, databases, and testing.

Selected signal

€1.6M in non-dilutive EU project funding around the company, Siemens technology partnership, participation in the Google DeepMind Accelerator, and TRL 6–7 industrial validation.

Primary stack

Machine learningPyTorch, JAX, TensorFlow

APIsFastAPI, Flask, Pydantic

DataPostgreSQL, SQLAlchemy, Alembic, Airflow

ScientificNumPy, SciPy, MPI, OpenMP

DeliveryDocker, CI/CD, AWS, testing

LanguagesPython, C++, JavaScript

Operating principle

Make the complex system legible to the person who has to use it.

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Daniel Haas

AI ENGINEER · DATA SCIENTIST · PHYSICIST

Oslo, Norway · (+47) 46 56 56 51 · danielhaas.lima@gmail.com

Summary

Master's in Computational Science and a Bachelor's in Physics with an emphasis in Mathematics. AI engineer applying machine learning to robotic welding and metal additive manufacturing, while building the supporting product, data, and deployment infrastructure.

Experience

Jul 2024 — presentAI Engineer · 3D Components AS, Oslo

Lead AI-driven solutions for robotic welding and metal additive manufacturing.
Build target-driven process-parameter selection and uncertainty-aware optimisation workflows.
Design application architecture spanning desktop UI, APIs, databases, and model deployment.
Maintain material and experiment data supporting production machine-learning models.

Feb 2024 — Oct 2024Machine Learning Intern · Kristiania University College

Refined and published a pipeline for automatic cardiac MRI structure annotation using modern segmentation architectures and topology-preserving losses.

Jan 2024 — presentTeaching Assistant · University of Oslo

Computational Physics II: remodelled course material from C++ to Python/JAX, graded projects, and supported students.

Aug 2023 — Jan 2024Teaching Assistant · University of Oslo

Applied Data Analysis and Machine Learning: facilitated sessions, graded projects, and supported students.

Jun 2023 — Aug 2023Summer Intern · Simula

Developed a machine-learning pipeline for automatic cardiac MRI structure identification and annotation.

Apr 2021 — Aug 2022Data Scientist · Inmetrics, São Paulo

Built time-series forecasting and anomaly-detection models that reduced downtime and accelerated reporting.
Modelled data pipelines, databases, and ETL processes.
Deployed Flask applications used daily by more than 20 companies.

Education

2022 — 2024M.Sc. Computational Science: Physics · University of Oslo

Thesis: Deep Learning Methods for Quantum Many-Body Systems.

2017 — 2021B.Sc. Physics · UFMG, Belo Horizonte

Complementary specialization in Mathematics.

Skills

Machine learning: PyTorch, JAX, TensorFlow, computer vision, time series, optimisation
Data and APIs: FastAPI, Flask, Pydantic, SQLAlchemy, Alembic, Airflow
Scientific computing: NumPy, SciPy, C++, MPI, OpenMP
Engineering: Docker, CI/CD, testing, AWS, JavaScript

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