Deep Learning Expert in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Deep Learning
David Onaiyekan
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Muntaha Shams
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Puranjan Bandyopadhyaya
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Pawan Saxena
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Uddipan Basu Bir
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Musaib Parray
Last position:
Research Intern – Exploring Reasoning with Diffusion Models at Machine Learning and Perception group, FAU Erlangen-Nürnberg
- Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
- Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.
Ashmi Jha
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
Ekaansh Khosla
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Discover over 15,000 top freelancers
Statistics of experts using Deep Learning
Aggregated from the professional profiles of matched freelancers.
Experience
7 years (Germany: 13 years)
Position duration
1.3 years (Germany: 2.1 years)
Positions per freelancer
5 (Germany: 8)
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
100% (Germany: 88%)
Certifications per freelancer
2
Most common languages
English, German, Bangla
Speak two or more languages
100% (Germany: 99%)
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 Nuremberg 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 Nuremberg using Deep Learning
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
Advanced Neural Network Engineering
Deep learning drives the most ambitious artificial intelligence projects today. Experts in this field build systems that can see, hear, and understand complex data patterns. From autonomous driving algorithms to generative AI systems, these professionals translate raw unstructured data into actionable business value.
Core Ecosystem and Frameworks
To build robust deep neural networks, specialists utilize a mature ecosystem of open-source tools. They write highly optimized code to train models across distributed systems. Key technologies include TensorFlow, PyTorch, Keras, and specialized libraries for natural language processing and computer vision.
Typical Deep Learning Deliverables
- Custom convolutional neural networks for automated visual inspection
- Fine-tuned transformer models for specialized text analysis
- Reinforcement learning pipelines for industrial robotics
- Model compression and optimization for edge devices
Local Industry Applications in Nuremberg
In the Nuremberg metropolitan region, companies in industrial automation, medical technology, and automotive sectors leverage artificial neural networks. Local experts design intelligent systems for factory floor quality control and medical image diagnostics, aligning with regional engineering standards.
When to Bring in External Specialists
Training deep neural networks requires highly specialized mathematical knowledge and infrastructure experience. Organizations bring in freelance experts to accelerate model training, debug convergence issues, or set up secure MLOps pipelines without the overhead of long-term research hiring.
Key Qualifications of Top Professionals
Outstanding specialists combine deep mathematical knowledge with solid software engineering principles. They understand how to manage large datasets, configure GPU clusters for training, and write clean, reproducible code. They also bridge the gap between academic research and commercial deployment.
Frequently asked questions
Everything clients usually want to know about Deep Learning, in one place.
A deep learning specialist focuses on designing, training, and deploying multi-layered artificial neural networks. They work on complex unstructured data like images, audio, and text to solve problems that traditional machine learning algorithms cannot handle efficiently.
Most deep learning professionals rely on PyTorch or TensorFlow as their primary libraries. They also utilize high-level APIs like Keras, alongside Python-based data science libraries such as NumPy, Pandas, and Scikit-Learn to prepare training datasets.
Traditional machine learning often requires manual feature engineering to extract patterns from data. In contrast, deep learning models automatically learn representations from raw data through multiple hidden layers, requiring significantly more computing power and data to achieve high accuracy.
Nuremberg is a major hub for industrial automation, logistics, and medical technology. Local projects for deep learning experts often involve automated optical inspection in manufacturing, predictive maintenance for machinery, and AI-assisted medical imaging diagnostics.
Yes, most software engineering and training tasks for deep learning can be performed remotely. However, for hardware-integrated projects in Nuremberg's manufacturing sector, occasional on-site visits to integrate models with physical machinery or local GPU clusters can be highly beneficial.
While English is the standard language for AI research and code, local coordination in German is often preferred by engineering teams in Nuremberg. Most deep learning specialists in the region are fluent in English, and many also offer native or professional German communication skills.
Quality is measured by model accuracy, generalization on unseen data, and execution speed. A skilled deep learning professional delivers reproducible training pipelines, well-documented code, and optimized inference setups that do not waste expensive cloud computing resources.
Scalable deep learning requires extensive knowledge of cloud platforms like AWS, Google Cloud, or Azure. Specialists must know how to spin up GPU instances, manage large-scale cloud storage, and configure containerized deployments using Docker and Kubernetes.
The average hourly rate of freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects is 43 €, which corresponds to a daily rate of about 341 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects have 7 years of professional experience, with a single engagement typically lasting around 1.3 years.
The most common languages among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are English (100%), German (88%), and Bangla (25%).
The most common industries among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are Information Technology (100%), Education (75%), and Manufacturing (50%).
The most common business areas among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are Information Technology (88%), Research and Development (88%), and Product Development (75%).
Main locations of FRATCH Experts, who have recently used Deep Learning
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.
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Hamburg 
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Philipp Thomaschewski
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