Technical & Professional Skills
A comprehensive overview of my computational, biological, and analytical capabilities.
My multidisciplinary background has allowed me to develop a versatile skill set bridging computational biology, data science, and life sciences.
Translating complex biological questions into robust computational workflows. Adapting experimental designs and ensuring research follows FAIR principles.
Writing comprehensive technical reports, creating publication-ready figures, and delivering impactful oral presentations in both English and French.
Successfully transitioning across disciplines—from wet-lab plant pathology to dry-lab computational biology and clinical environments.
Daily proficiency in using state-of-the-art AI assistants (e.g. Claude Code, Copilot, custom agents) for code generation, advanced debugging, and rapid prototyping. Experienced in building and integrating LLM-based workflows, REST APIs, and Model Context Protocol (MCP) servers to accelerate learning and automate software engineering tasks.
- Curiosity & Global Vision
- Exceptionally curious and broad-minded, actively exploring diverse scientific fields (biology, data science, clinical medicine, AI) to build an integrative, global perspective.
- Perseverance
- Empathy
- Scientific Rigor
Languages
Python - Data analysis, machine learning, scripting R - Statistical analysis, bioinformatics Bash / Shell - Workflow automation, server management SQL & Supabase - Relational databases & backend Cypher & Neo4j - Graph databases & knowledge graphs Java - Basic software development Rust & C (Basic) - System programming, memory management Swift & iOS (Basic) - Mobile app development
Tools & Infrastructure
Docker & Dev Containers - Containerization, reproducibility Environments & Dependencies - Conda, uv, renv, pixi Git & GitHub/GitLab - Version control, collaboration HPC / Slurm - High-performance computing clusters Nextflow - Bioinformatics pipeline automation Quarto, Typst, Marimo - Literate programming & reporting Knowledge Management - Obsidian (PKM), Zotero (Bibliography) VPS & Cloudflare (Basic) - Server deployment & web security API Integration & MCP (Basic) - REST APIs, Model Context Protocol integration
Statistical Modeling
- Experimental Design & Hypothesis Testing (Student, Chi-2, Parametric/Non-parametric, Post-hoc)
- Multivariate Analysis & PCA
- Regression Models (Linear, Logistic, GLM)
Machine and Deep Learning
- Frameworks & Libraries
PyTorch & TensorFlow (Basic) - Deep learning Scikit-learn (sklearn) - Machine learning, modeling, clustering Hugging Face (Transformers) - NLP & Generative AI models
- Computer Vision & Image Analysis
- Data prep: annotation & augmentation with OpenCV
- CNN, YOLO
- Generative AI & LLMs (Applied Usage, Agentic AI, Local Models/Ollama)
- RAG & Knowledge Graphs (Vector Databases, LLM Integration)
- Data Ingestion & Document Parsing: Playwright (web automation), Beautiful Soup, MinerU (AI layout extraction for RAG)
- Machine Learning & Clustering (Random Forests, SVMs, K-means, Hierarchical)
Data & Sequence Analysis
- NGS Quality Control & Alignment (Trimming, FastQC, Read mapping)
- Single-cell RNA-seq (Scanpy, Seurat)
- Phylogenomics & Sequence Alignment (OrthoFinder, MAFFT, IQ-TREE, GeneRax)
NoteClinical Observation & Familiarity
Observational Internships
- Medical Genetics (2 weeks, CHU Rennes)
- Surgical Oncology (2 days, Clinique de l’Anjou)
Initial Familiarity & Medical Knowledge
- Healthcare Settings: First exposure to consultations, patient interaction, and clinical environments.
- Team Collaboration: Observing multidisciplinary team meetings (RCP) to see how specialists coordinate care.
- Medical Knowledge: Begun self-learning and initial preparation of foundational medical school coursework.
NoteLife Sciences, Academic Knowledge & Practical Biology
Academic Knowledge & Disciplines
- Genetics & Genomics: Gene regulation, molecular cloning, genome analysis, and inheritance patterns.
- Microbiology & Immunology: Host-microbe interactions, phytopathology, mycology, and foundations of immunology.
- Molecular & Cell Biology: Cell signaling, molecular biology techniques, and plant physiology.
Wet Lab & Experimental Methods
- Wet Lab & Imaging: Wet-lab techniques, microscopy & macroscopic imaging, in vitro & in vivo cultivation.
- Phenotyping & Biosafety: High-throughput phenotyping & robotic imaging, Biosafety Level 2 (S2) greenhouse operations, GMO manipulation, and plant pathogen inoculation.
Computational Biology & Data Science
- Programming Languages: Python (Data analysis, ML, scripting), R (Statistics, Bioinformatics), Bash/Shell, SQL, Neo4j (Graph databases), Java, Rust & C (Basic), Swift/iOS (Basic).
- Tools & Infrastructure: Docker & Dev Containers, Environments (Conda, uv, renv, pixi), Git & GitHub, HPC (Slurm clusters), VPS & Cloudflare, Nextflow pipelines, Quarto, Typst, Marimo.
- Machine & Deep Learning: PyTorch, TensorFlow, Scikit-learn, Hugging Face (Transformers), Computer Vision (OpenCV, YOLO), Generative AI & local LLMs (Ollama), RAG & Knowledge Graphs, Data Ingestion (Playwright, MinerU).
- Data & Sequence Analysis: Single-cell RNA-seq (Scanpy, Seurat), NGS QC & Alignment, Phylogenomics & Sequence Alignment (OrthoFinder, MAFFT, IQ-TREE).
Life Sciences & Clinical Exposure
- Clinical Familiarity: Short-term observations in Medical Genetics (CHU de Rennes) and Surgical Oncology (Clinique de l’Anjou), observing multidisciplinary team meetings (RCP). Begun self-learning of foundational medical school coursework.
- Academic Foundations: Solid background in Molecular & Cell Biology, Genetics & Genomics, Microbiology, Immunology foundations, and Plant Pathology.
- Wet Lab & Microscopy: Wet-lab techniques, microscopy & macroscopic imaging, in vitro/in vivo cultivation, GMO manipulation.
- Experimental Biology & Biosafety: High-throughput phenotyping & robotic imaging, Biosafety Level 2 (S2) greenhouse operations, plant pathogen inoculation (Erwinia amylovora, Alternaria brassicicola).
Professional & Soft Skills
- Core Capabilities: Analytical Thinking (FAIR principles), Scientific Communication (reports, oral presentations, Quarto formatting), Adaptability across wet-lab and dry-lab.
- AI-Augmented Development: Pro in using AI pair-programmers (Claude Code, custom agents, APIs, MCP servers) for rapid software prototyping and debugging.
- Languages: French (Native), English (Fluent).
- Strengths: Curiosity & Global Vision, Perseverance, Empathy, Scientific Rigor.