Aerzener Maschinenfabrik
Hybrid (Hameln, Germany)
The challenge
Acquisition, processing, structuring and visualization of large data sets
Collaboration with the research and development department for the continuous improvement of physical products based on the knowledge gained
Modeling the data using Python using relevant libraries such as Pandas, Tensor Flow and scikit-learn
Creation and validation of machine learning models, especially neural networks for fluid and positive displacement machines, with extensive experience in the entire machine learning lifecycle, including data preparation, model training, hyperparameter tuning, model evaluation and deployment
Application of statistical theories and methods to analyze and evaluate data
Experience with DevOps technologies such as Docker, Kubernetes and CI/CD
Experience with cloud technologies, especially with the Microsoft Azure platform
Preparation of detailed presentations of results for consulting and information purposes as well as...