Master Project BRIDGE (PFE or master intership, 6 months)

Context and Motivation

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The computational biology teams at our laboratory regularly develop prototypes for analysis and modeling tools in Python. These scripts are often limited to internal use, making it difficult to share, reuse, and integrate them into larger workflows.

Objectives

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In the spirit of open science, collaborative work on a larger scale, and ensuring the quality of data processing pipelines, we need to set up a software infrastructure that can automatically convert these prototypes into stable, documented, and FAIR-compliant web APIs, compatible with international standards like SmartAPI.

While developping a DevOps pipeline, the student will perform research in one of these areas

• **Feature Extraction from a Python Prototype: Static analysis, input/output inference, and automatic suggestion of “natural” endpoints.
• **Collaborative Notebook Generation: Semi-automatic transformation of scripts into structured and reproducible notebooks.
• **FAIR Metadata: Semi-auutomated extraction of descriptions, parameters, provenance; alignment with EDAM/Bioschemas publication on

Expected Outcomes

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The expected outcomes include:

• A functional platform capable of deploying multiple APIs requiring computational power from different projects.
• Significant reduction in the time between developing a prototype and making it publicly available for evaluation.
• Participation in writing a publication on this new tool.
• Improved reproducibility and dissemination of tools developed within our team, with a methodology that can be generalized to other lab projects.

Candidate Profile

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To achieve this project, we are looking for a candidate with the following qualifications:

• Education in computer science or software engineering (Master’s level 2).
• Strong Python skills and good mastery of object-oriented programming.
• Knowledge of web frameworks like FastAPI, Flask, or Django REST Framework.
• Familiarity with DevOps principles: CI/CD, unit tests, continuous integration, Git/GitLab versioning.
• Practical experience with Docker and, ideally, Kubernetes.
• Understanding of scientific environments (dependency management, research scripts).
• Ability to document, structure, and automate code.

What We Offer

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We offer a work environment that combines academic research and software engineering. The intern will have the opportunity to design a complete DevOps infrastructure, used later by several lab projects. This work can lead to a scientific publication.