General Software Development

• Applications for Microsoft® Windows (C# / .Net, C++)
• Automotive Software Components for Embedded Linux Systems according to SPICE and MISRA rules (C++)
• Performance Optimization for .Net Applications using Task Parallel Library (TPL/.NET).
• Performance Optimization for Script Languages (Python, Matlab, Scilab) using external C++ Libraries
• Microsoft® Office Integration (VBA)

Data Science / Machine Learning / Artificial Intelligence

• Defect Classification for Loudspeakers and Electric Window Motors
• Feature Extraction (e.g. wavelet packet transformation)
• Dimentionality Reduction and feature selection (e.g. PCA, Auto Encoders, Lasso)
• Clustering (k-Means, Fuzzy c-Means, Gath-Geva, Expectation Maximization, Hierarchical)
• Statistics and Visualisation (e.g. Matplotlib, Seaborn, Spectrogram, Scaleogram, Waterfall Plot, t-SNE)
• Classification (Logistic Regression, SVM, Random Forest, Decision Trees, Boosted Trees)
• Parallel Computing using PySpark
• Deep Learning (NN, CNN, RNN/LSTM, Auto Encoders) with TensorFlow und Keras

Customizations for the Klippel Measurement System

• Processing of Klippel Databases (kdb, Klippel Automation)
• Remote Control the Klippel QC System (IO-Monitor)
• New Features for the Klippel QC Scripts (Scilab)