🚀 My Journey
Welcome to my journey—a timeline that chronicles my educational and professional experiences. Explore the milestones and achievements that have shaped my path, from learning and growth to exciting opportunities. This page offers insights into my story and the adventures that have led me to where I am today.
Feel free to navigate through the timeline to discover more about my educational pursuits, career endeavors, and the valuable lessons I’ve gained along the way.
Let’s embark on this journey together! 🌟
💼 Experience :
Research Assistant
Company: Fraunhofer Institute IPA
Time: Jan. 2025 – Present
Description:
Department: Automation Planning Focus on robotic hands for complex assembly processes in industrial applications. Object tracking with pose estimation using a multi-camera setup.
Skills:
ROS, Python, Computer Vision, Machine Learning, Image Processing, Segmentation
Bachelor Thesis: AI and Large Language Models
Company: SCHUNK – Hand in Hand for Tomorrow
Time: Mar. 2024 – Sept. 2024
Description:
Department: Digital Office, IPAI Evaluation and implementation of a retrieval-augmented generation (RAG) approach for product data search using generative AI. Internal hackathon: Robotic bin picking using LLMs and few-shot detection.
Skills:
Python, LangChain, Vector DBs, ChatGPT, PyTorch, Docker, Linux
Software Engineer (Working Student)
Company: IDS Imaging Development Systems GmbH
Time: Sept. 2023 – Mar. 2024
Description:
Department: New Technologies Worked on IDS lighthouse, a cloud-based AI vision studio: - Integrated autolabelling service - Dockerized all services for dev & deployment - Evaluated and integrated SOTA detection models
Skills:
Python, Docker, Git, TensorFlow, Computer Vision, Segmentierung
Intern – New Technologies
Company: IDS Imaging Development Systems GmbH
Time: Mar. 2023 – Sept. 2023
Description:
Department: New Technologies - Created REST interface examples for NXT cameras - Built dashboard for monitoring IDS lighthouse performance - Trained YOLOv8 model and developed auto-labeling service - Internal hackathon: ML canteen occupancy tracking
Skills:
C/C++, Python, TypeScript, React, PyTorch, Docker, REST, TensorFlow
Test Engineer (Working Student)
Company: Bosch
Time: Mar. 2022 – Feb. 2023
Description:
Department: Chassis Systems Control Worked on engineering, testing, and development of hydraulic systems.
Skills:
Engineering, Testing, R&D
Student Assistant
Company: Heilbronn University – Center for Industrial AI
Time: Jun. 2022 – Dec. 2022
Description:
Assisted in hardware prototyping and embedded projects: - Arduino and Raspberry Pi development - Coded in C, C++, Python - Co-developed the 'Wasserdrucker' UI with wireless features Presented at the KI Festival in Heilbronn
Skills:
C, C++, Python, Arduino, Raspberry Pi
🎓 Education :
Master of Science – Autonomous Systems
Institution: University of Stuttgart
Time: Sept. 2024 – Aug. 2026
Description:
- In-depth literature review on the use of generative AI in large-scale codebases.
Skills:
Computer Vision, Artificial Intelligence (AI), LLM
Bachelor of Engineering – Mechatronics and Robotics
Institution: Heilbronn University
Time: Nov. 2020 – Aug. 2024
Description:
Bachelor's thesis GPA: 4.0/4.0 Topic: Retrieval-augmented generation (RAG) for product data search with generative AI Seminar: Time series prediction of chaotic double pendulum using neural networks Projects: - Traffic sign recognition with CNN - Chatbot using LSTMs
Skills:
TensorFlow, Machine Learning, Python, Fusion 360, C++, PyTorch, Git, Time Series Analysis, Image Processing, Robotics, MATLAB, CATIA
💡 Personal Projects
Vision-Action Transformers for basic assembly tasks (In the making)
This ongoing project focuses on generating high-quality, scalable training data in Isaac Gym to train Action Chunking Transformers for robotic assembly. The goal is to move beyond imitation learning by simulating diverse grasp and manipulation behaviors, then use this data to train a ACT. Future steps involve expanding to vision-action-language models and deploying the full pipeline back into Isaac Gym for closed-loop robotic control.
Features :
- Synthetic trajectory and vision data generation using Isaac Gym with camera-mounted robotic arms.
- ...
Tech Stack :
Traffic Sign Recognition with YOLOv8
Developed as part of a university project at Heilbronn University, this real-time traffic sign recognition system uses YOLOv8n for fast and robust detection. It handles challenging conditions like occlusion, poor lighting, and complex backgrounds by leveraging a custom synthetic dataset, multi-stage classification, and real-time frame filtering.
Features :
- Real-time traffic sign detection using YOLOv8n (Nano version for speed and efficiency).
- Custom synthetic dataset generation with COCO backgrounds and heavy augmentation.
- Two-stage classification specifically for speed limit signs.
- Frame caching logic to reduce false positives during inference.
- Visualization via UI overlay: persistent speed sign display + rotating multi-sign view.
- Trained on 3000+ synthetic images and validated with GTSDB and dashcam footage.
- Fast inference: ~0.06–0.09 seconds/frame.
Tech Stack :
Stabled Grounding SAM
Stabled Grounding SAM is a powerful tool for generating synthetic datasets with pre-segmented images. It combines Stable Diffusion, Grounding DINO, and Segment Anything to create annotated datasets from just a single input image and a label file.
Features :
- Generates synthetic images from a single input image using Stable Diffusion's `img2img`.
- Automatically detects and labels objects using Grounding DINO.
- Refines segmentations using Meta’s Segment Anything model.
- Outputs datasets in YOLO format for easy training integration.
- Great for quickly building vision datasets without manual labeling.
Tech Stack :
Get in Touch! 👋
Hello there, fellow tech enthusiast! 🚀
I’m thrilled that you’ve dropped by. Whether you have a burning question, an exciting project idea, or just want to chat about all things code, I’m all ears.
Let’s make this the start of a fantastic conversation. Feel free to reach out, and I’ll get back to you as soon as I can.
Cheers to the future collaborations and coding adventures! 🌟
📧 Reach out via email:
Email : marco.menner@web.de
🌐 Connect with me online:
Looking forward to hearing from you soon! 📩