PhD engineer with 7+ years of experience in Python, machine learning, data science and software development. My background is in scientific R&D, where I worked on complex experimental data, ML pipelines and user-facing software. Over time, my work expanded toward backend development, APIs, deployment, automation and generative AI. I enjoy building practical, maintainable tools that connect technical problems with real user needs.
Python • FastAPI • Flask • REST APIs • SQL • Docker • Linux • Git / CI • pytest
scikit-learn • PyTorch • TensorFlow / Keras • Pandas • NumPy
Machine learning • Deep learning • RAG • Embeddings • LLMs • Ollama • Whisper • Model evaluation
Complex and high-dimensional data • HDF5 • Data visualization
Workflow automation • n8n • Monitoring • Technical documentation • Requirements analysis
Python • FastAPI • Ollama • Embeddings • Hybrid Search
A local RAG system for scientific literature with source-grounded answers, citations, hybrid retrieval and comparison of retrieval strategies.
Python • Whisper • LLMs
A local application for meeting recording, speech-to-text transcription and structured note generation.
Python • FastAPI • scikit-learn • pytest • Docker
A reproducible ML API for molecular classification from SMILES, including training, validation, testing and documentation.
🔒 Note: Most of my recent industrial R&D work is confidential. The examples below illustrate some of the technical problems I have worked on across software, machine learning and scientific data.
- Machine learning for noisy high-resolution signals
Designed preprocessing, data augmentation and model evaluation workflows for PFAS identification from complex ¹⁹F NMR spectra, including simulated spectra and noise injection. Achieved 96% precision and 95% recall, with a strong focus on error analysis and robustness. - Python SaaS development for expert users
Developed and maintained Python/Flask applications within a SaaS platform, translating expert user needs into specifications and maintainable software features. Contributed to Docker/Linux deployment, Git/CI workflows, monitoring, documentation and technical support. - Large-scale data processing and memory-efficient workflows
Worked on 2D FT-ICR MS deconvolution for very large datasets, addressing RAM limitations through out-of-core processing with HDF5, block-wise data loading and dynamic resolution selection. - Deep learning for automated signal attribution
Designed and trained neural networks for automated NMR signal attribution, reaching 89.5% accuracy on experimental data and outperforming the previous benchmark tool. - Reproducible ML and data workflows
Built reproducible pipelines for data preparation, training, hyperparameter optimization, validation and model evaluation, with attention to traceability, documentation and FAIR data practices. - Collaborative R&D and technical project work
Contributed to multidisciplinary R&D projects, technical documentation, funding proposals and coordination between scientific, technical and user-facing stakeholders.
My background is rooted in scientific R&D, but the core of my work has increasingly focused on Python development, machine learning, data-intensive software and user-oriented tools. I am particularly interested in applying these skills to broader technical and industrial problems.
Python development • Backend / APIs • Machine Learning • Applied AI • RAG / LLM applications • Data-intensive software • Scientific software • Technical project roles • AI/Data project coordination
- LinkedIn: www.linkedin.com/in/laura-duciel
- Email: laura.duciel@hotmail.fr
- Location: Illkirch-Graffenstaden, France
"Turning complex data and user needs into practical Python, ML and AI solutions."