Convolutional Neural Network Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Convolutional Neural Network
René Welland
Last position:
Conference Operator at Brähler Systems GmbH
- Developed the iOS/Android Delegate App and the Conference Operator
- Updated and developed a user-friendly conference environment and real-time video streaming
- Optimized the overall conference experience by implementing customizable features for flexible setup
- Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience
Raghu Ram Vadali
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Martin Ratajczak
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Daniel Carton
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
Technologies: Kubernetes (K3s, RKE2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3s), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Suzan Kanigür
Last position:
Digital Marketing & Analytics; Identity and Access Management at Rohde & Schwarz GmbH & Co. KG
- Webinar moderation.
- Created quarterly KPI reports using Adobe Analytics, Sprinklr, and M4C.
- Researched and proposed AI tools for marketing process optimization; pitch decks, integration planning.
- Defined customer personas, analyzed competitor social media activity, and improved CRM data quality.
- Executed IDM tasks related to user provisioning, access reviews, and role management.
- Performed integration testing with MidPoint, Active Directory (AD), and SAP systems.
- Supported identity lifecycle management across onboarding, role changes, and deprovisioning.
Robert Darius
Last position:
Head of Data Business Development at Hubert Burda Media
- Building and leading team with budget responsibility, reporting to C-level
- Developing new double-digit million revenue stream (Retail Media) for subsidiary companies
- Leading group-wide Generative AI adoption
Marwa Hamrouni
Last position:
Computer Vision Project
- Developed a convolutional neural network (ConvNet)-based model that achieved 95% accuracy in traffic sign recognition and classification.
- Programming language: Python 3.7.
- Libraries: Numpy, matplotlib, scikit-learn, scikit-image.
- Deep learning framework: Tensorflow.
Adithya Balaji
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using Convolutional Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 11 years)
Position duration
2.5 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 7)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Finance
Bachelor's degree or higher
90% (Germany: 98%)
Master's degree or higher
90% (Germany: 87%)
Doctorate
30% (Germany: 15%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich using Convolutional Neural Network
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
CNN basics
A Convolutional Neural Network, often called a CNN or ConvNet, is built for data with a grid shape, especially images and video frames. It learns local patterns first, then combines them into higher-level features. Companies use it for inspection, classification, detection, and recognition tasks.
What it handles
- Image classification for product, medical, or document data
- Object detection and segmentation in camera feeds
- Visual quality control on factory lines
- OCR support and document understanding
- Video analysis for tracking and event detection
Tooling and stack
Strong professionals working with CNNs usually use Python, PyTorch, TensorFlow, Keras, and OpenCV. They also know how to manage training data, augmentation, GPU workloads, and experiment tracking. In Munich, this often fits teams in automotive, robotics, industrial imaging, medtech, and other vision-heavy work.
When freelance help fits
Freelance expertise is useful when a team needs a model built, repaired, or moved into production quickly. It also helps when internal specialists know the domain but need support with architecture choices, label quality, or deployment. Short engagements work well for audits, proofs of concept, and hard debugging.
What good experts do
A strong CNN specialist does more than tune layers. They define the right target, check data balance, spot leakage, and measure failure cases clearly. They can explain why a model works, where it breaks, and what to change when new data arrives.
Delivery outcomes
Good work with CNNs should leave behind usable assets, not just a notebook. That usually means trained models, preprocessing code, evaluation reports, and a clear path for inference in batch, API, or edge settings. Clean handover matters so teams can keep improving the system after the project ends.
Frequently asked questions
Before you brief your next project: the most common questions about Convolutional Neural Network.
A Convolutional Neural Network is used for visual pattern tasks where the shape and position of features matter. Common uses include image classification, defect detection, face or object recognition, and document image analysis. It is also useful for video frames when a system needs to spot patterns over time.
Yes, CNN and ConvNet usually refer to the same family of models. Searchers often use both names, so a freelancer should be comfortable with each term. In conversations, many teams still say CNN even when their code uses TensorFlow or PyTorch.
A Convolutional Neural Network is often simpler and easier to justify for image tasks with strong local structure. Transformers can perform well too, especially on larger or more complex vision problems, but they may need more data and tuning. The best choice depends on the data, latency needs, and deployment target.
A strong Convolutional Neural Network specialist usually knows Python, data preprocessing, and one major framework such as PyTorch or TensorFlow. OpenCV, image augmentation, labeling workflows, and model evaluation are also important. For production work, deployment knowledge and basic MLOps skills help a lot.
A Convolutional Neural Network project works best when the expert has a clear task definition, sample data, and a sense of the target environment. They do not need every detail on day one, but they do need to know what counts as a correct output. Good setup saves time later, especially if the model will run in a real system.
Most Convolutional Neural Network work can be done remotely if the data access and review process are set up well. On-site time in Munich can help when the project depends on lab equipment, factory cameras, or close work with domain experts. Many teams use a mix of both.
Look for clear examples of problem framing, data handling, and error analysis, not just model names. A strong Convolutional Neural Network expert can explain trade-offs, show how they tested for overfitting, and describe what they changed when results were poor. Ask how they handled labeling noise, class imbalance, and deployment constraints.
Before starting, a Convolutional Neural Network freelancer should ask about the data source, class definitions, and the real decision the model supports. They should also confirm how results will be measured and where the model will run. If the task involves Munich teams, they should ask whether review meetings and handover sessions are expected on-site or remote.
The average hourly rate of freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects, 90% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are German (100%), English (100%), and French (45%).
The most common industries among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (91%), Automotive (55%), and Education (55%).
The most common business areas among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (100%).
Main locations of FRATCH Experts, who have recently used Convolutional Neural Network
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
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Philipp Thomaschewski
FRATCH CEO
