Machine Learning Engineer mit starkem Fokus auf Computer Vision, sensorbasierter Wahrnehmung sowie datengetriebener Analyse physikalischer Messsignale
Aktualisiert am 06.07.2026
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 06.07.2026
Verfügbar zu: 100%
davon vor Ort: 100%
Computer Vision
Machine Learning
Industrielle Bildverarbeitung
Deep Learning
Sensortechnik
Signalverarbeitung
Zeitreihenanalyse
Automotive
Sicherheitstechnik
Elektronikentwicklung
Fahrerassistenzsystem
Sensorsignalverarbeitung
Data Scientist
Laborgeräte
Messtechnik
Regelungstechnik
Python
MATLAB
Robotik
MLOps
Feature Engineering
DSGVO
Verkehrszeichenerkennung
German
native
Turkish (native)
native
English
professional proficiency
Spanish
basic
French
basic

Einsatzorte

Einsatzorte

Blumberg (Baden) (+200km)
Deutschland, Schweiz
möglich

Projekte

Projekte

4 Jahre
2022-02 - 2026-01

Development of end-to-end ML pipelines including preprocessing

Academic Research Associate
Academic Research Associate
  • Development of end-to-end ML pipelines including preprocessing, feature engineering, model training and evaluation
  • Designed and implemented modular ML pipelines from data preprocessing to model evaluation
  • Applied best practices for model validation, robustness analysis, and generalization on unseen data
  • Time-series data analysis
  • Supervision of bachelor and master theses and instruction in machine learning, signal processing, and safety engineering laboratories.
  • Collaborated with interdisciplinary teams to integrate ML methods into applied engineering contexts
Furtwangen University of Applied Sciences
6 Monate
2024-09 - 2025-02

Generalizable Features for Non-Intrusive Sitting Posture Recognition

  • Multiclass classification using 32×32 pressure-mat images
  • Feature Extraction, classical ML and CNN-based modelling
  • User independent evaluation

6 Monate
2023-09 - 2024-02

Development of ML models

Posture Estimation by Analysing the Pressure Distribution on a Cushion Using Machine Learning
Algorithms
  • Development of ML models for sensor-based perception using pressure-mat image data (32×32 spatial sensor grids)
  • Feature engineering and CNN-based modeling for multiclass classification
  • Cross-validation and robustness evaluation on user-independent datasets
1 Jahr 2 Monate
2018-11 - 2019-12

Performance testing of pedestrian detection systems

System Test Engineer (ADAS / FAS)
System Test Engineer (ADAS / FAS)
  • Performance testing of pedestrian detection systems under EURO NCAP conditions
  • Analysis and evaluation of real-world multi-sensor data (radar, lidar, camera) for perception system testing
  • Commissioning and operation of target systems (GST, 4a), robotics, and OxTS reference measurement systems
  • Commissioning and operation of additional measurement equipment (Vector CANalyzer) and AVAD2 in the pre-rating environment
  • Hands-on experience with data acquisition pipelines and measurement systems in real-world testing scenarios
  • Execution of NCAP and NHTSA/IIHS pre-ratings in coordination with the responsible engineering department
  • Maintenance and repair of target systems (GST/GVT, robotics, 4a, AEB test systems, BSM Hase)
  • Execution of international test drives in coordination with customers
  • Analysis of sensor behavior in multi-sensor perception systems and sensor fusion pipelines (radar, lidar, camera)
  • Evaluation of robustness and system safety in safety-critical ADAS environments
CMORE Automotive GmbH

Aus- und Weiterbildung

Aus- und Weiterbildung

09/2026

PhD in Computer Science

Universit´e de Haute-Alsace, France


Key Focus:

  • Multiclass Classification using Pressure-Mat Image Data
  • Signal and Image Processing
  • Time-Series Data Analysis
  • Deep Learning; Model Validation
  • Dissertation: on request


Mechatronic Systems

M.Sc. (Grade: 2.0)

Furtwangen University of Applied Sciences, Germany


Mechanical Engineering & Mechatronics

B.Sc. (Grade: 2.3)

Furtwangen University of Applied Sciences, Germany

Kompetenzen

Kompetenzen

Top-Skills

Computer Vision Machine Learning Industrielle Bildverarbeitung Deep Learning Sensortechnik Signalverarbeitung Zeitreihenanalyse Automotive Sicherheitstechnik Elektronikentwicklung Fahrerassistenzsystem Sensorsignalverarbeitung Data Scientist Laborgeräte Messtechnik Regelungstechnik Python MATLAB Robotik MLOps Feature Engineering DSGVO Verkehrszeichenerkennung

Produkte / Standards / Erfahrungen / Methoden

Profile

  • Machine Learning Engineer ? Sensor Data & Perception Systems with a strong focus on computer vision, sensor-based perception, and data-driven analysis of physical measurement signals. Experienced in developing end-to-end ML pipelines for high-dimensional time-series and image-based sensor data, including preprocessing, feature engineering, model training, and validation. 
  • Background in signal and image processing as well as real-world multi-sensor systems in safety-critical environments (ADAS). Highly motivated to translate machine learning prototypes into robust, productionready solutions and to further develop expertise in MLOps and scalable data processing systems.


Machine Learning

  • Supervised & Unsupervised Learning
  • Deep Learning using Neural Networks (NNs) and Convolutional Neural Networks (CNNs)
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (kNN)
  • Random Forest
  • Hybrid ML Modeling
  • Feature Engineering
  • Model Evaluation
  • Cross-Validation


Signal & Image Processing

  • Time-Series Data Analysis
  • Spatial Ratio Analysis
  • Data Segmentation
  • Preprocessing
  • Feature Extraction
  • Dimension Reduction


MLOps & Data Engineering

  • Model versioning
  • reproducibility
  • pipeline structuring
  • experiment tracking

Programmiersprachen

Python
NumPy, pandas, scikit-learn, TensorFlow, Keras, OpenCV
MATLAB
C++

Einsatzorte

Einsatzorte

Blumberg (Baden) (+200km)
Deutschland, Schweiz
möglich

Projekte

Projekte

4 Jahre
2022-02 - 2026-01

Development of end-to-end ML pipelines including preprocessing

Academic Research Associate
Academic Research Associate
  • Development of end-to-end ML pipelines including preprocessing, feature engineering, model training and evaluation
  • Designed and implemented modular ML pipelines from data preprocessing to model evaluation
  • Applied best practices for model validation, robustness analysis, and generalization on unseen data
  • Time-series data analysis
  • Supervision of bachelor and master theses and instruction in machine learning, signal processing, and safety engineering laboratories.
  • Collaborated with interdisciplinary teams to integrate ML methods into applied engineering contexts
Furtwangen University of Applied Sciences
6 Monate
2024-09 - 2025-02

Generalizable Features for Non-Intrusive Sitting Posture Recognition

  • Multiclass classification using 32×32 pressure-mat images
  • Feature Extraction, classical ML and CNN-based modelling
  • User independent evaluation

6 Monate
2023-09 - 2024-02

Development of ML models

Posture Estimation by Analysing the Pressure Distribution on a Cushion Using Machine Learning
Algorithms
  • Development of ML models for sensor-based perception using pressure-mat image data (32×32 spatial sensor grids)
  • Feature engineering and CNN-based modeling for multiclass classification
  • Cross-validation and robustness evaluation on user-independent datasets
1 Jahr 2 Monate
2018-11 - 2019-12

Performance testing of pedestrian detection systems

System Test Engineer (ADAS / FAS)
System Test Engineer (ADAS / FAS)
  • Performance testing of pedestrian detection systems under EURO NCAP conditions
  • Analysis and evaluation of real-world multi-sensor data (radar, lidar, camera) for perception system testing
  • Commissioning and operation of target systems (GST, 4a), robotics, and OxTS reference measurement systems
  • Commissioning and operation of additional measurement equipment (Vector CANalyzer) and AVAD2 in the pre-rating environment
  • Hands-on experience with data acquisition pipelines and measurement systems in real-world testing scenarios
  • Execution of NCAP and NHTSA/IIHS pre-ratings in coordination with the responsible engineering department
  • Maintenance and repair of target systems (GST/GVT, robotics, 4a, AEB test systems, BSM Hase)
  • Execution of international test drives in coordination with customers
  • Analysis of sensor behavior in multi-sensor perception systems and sensor fusion pipelines (radar, lidar, camera)
  • Evaluation of robustness and system safety in safety-critical ADAS environments
CMORE Automotive GmbH

Aus- und Weiterbildung

Aus- und Weiterbildung

09/2026

PhD in Computer Science

Universit´e de Haute-Alsace, France


Key Focus:

  • Multiclass Classification using Pressure-Mat Image Data
  • Signal and Image Processing
  • Time-Series Data Analysis
  • Deep Learning; Model Validation
  • Dissertation: on request


Mechatronic Systems

M.Sc. (Grade: 2.0)

Furtwangen University of Applied Sciences, Germany


Mechanical Engineering & Mechatronics

B.Sc. (Grade: 2.3)

Furtwangen University of Applied Sciences, Germany

Kompetenzen

Kompetenzen

Top-Skills

Computer Vision Machine Learning Industrielle Bildverarbeitung Deep Learning Sensortechnik Signalverarbeitung Zeitreihenanalyse Automotive Sicherheitstechnik Elektronikentwicklung Fahrerassistenzsystem Sensorsignalverarbeitung Data Scientist Laborgeräte Messtechnik Regelungstechnik Python MATLAB Robotik MLOps Feature Engineering DSGVO Verkehrszeichenerkennung

Produkte / Standards / Erfahrungen / Methoden

Profile

  • Machine Learning Engineer ? Sensor Data & Perception Systems with a strong focus on computer vision, sensor-based perception, and data-driven analysis of physical measurement signals. Experienced in developing end-to-end ML pipelines for high-dimensional time-series and image-based sensor data, including preprocessing, feature engineering, model training, and validation. 
  • Background in signal and image processing as well as real-world multi-sensor systems in safety-critical environments (ADAS). Highly motivated to translate machine learning prototypes into robust, productionready solutions and to further develop expertise in MLOps and scalable data processing systems.


Machine Learning

  • Supervised & Unsupervised Learning
  • Deep Learning using Neural Networks (NNs) and Convolutional Neural Networks (CNNs)
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (kNN)
  • Random Forest
  • Hybrid ML Modeling
  • Feature Engineering
  • Model Evaluation
  • Cross-Validation


Signal & Image Processing

  • Time-Series Data Analysis
  • Spatial Ratio Analysis
  • Data Segmentation
  • Preprocessing
  • Feature Extraction
  • Dimension Reduction


MLOps & Data Engineering

  • Model versioning
  • reproducibility
  • pipeline structuring
  • experiment tracking

Programmiersprachen

Python
NumPy, pandas, scikit-learn, TensorFlow, Keras, OpenCV
MATLAB
C++

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