> Markdown version of [/jobs/ext/2138867-rubisco-2-engineering-new-rubiscos](https://www.wearedevelopers.com/jobs/ext/2138867-rubisco-2-engineering-new-rubiscos). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# RubisCO.2 - Engineering new RubisCOs

- **Company:** Association Bernard Gregory
- **Location:** Paris, France (Remote available)
- **Salary:** €26,400.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Artificial Neural Networks, Bioinformatics, Computational Biology, Machine Learning, Support Vector Machine, Supervised Learning, High Performance Computing, Pytorch, Data Analytics, Variational Autoencoders, Markov, Unsupervised Learning
- **Published:** August 20, 2026
- **Apply:** https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/140060/job/rubisco-2-engineering-new-rubiscos-with-increased-co2-catalytic-activity

## About the Role

We seek a highly motivated DC with a strong background in physics at the Master level, including advanced statistical physics and theoretical soft matter, to contribute to the AI-driven design and analysis of RubisCO evolution. The candidate is also expected to have acquired solid training in artificial intelligence (unsupervised learning and generative models) and in data-driven modelling e.g. through internships and/or dedicated courses at the Master level. Experience with programming and high-performance computing is essential. In addition, familiarity with and/or interest for computational biology-such as sequence analysis, protein structure prediction, or evolutionary modelling-will be a significant asset, enabling the integration of experimental datasets into AI models. The ideal candidate will combine rigorous analytical skills with creativity to bridge physico-chemical-based modelling, AI, and biological data. Strong collaborative and communication skills
are required to work effectively within this interdisciplinary project., We expect DC to master advanced statistical physics techniques and concepts, including: phase transitions (order parameter, entropy-energy competition, critical phenomena), mean-field theory, dynamical processes (diffusion, master equation, path integrals, Monte Carlo Markov Chain sampling methods). Knowledge in disordered statistical physics, including spin glasses would be appreciated but is not mandatory. Solid theoretical and practical skills in machine learning are expected, covering in particular supervised learning (support vector machines and kernel methods), neural networks (stochastic gradient descent, deep architectures, physics-informed neural nets), unsupervised and generative learning (energy-based models, variational autoencoders). Experience in ML/AI programming in Python with PyTorch or equivalent will be required. We also expect DC to have acquired the basis of Bayesian inference (notions of likelihood, prior, posterior, ML and MAP estimators).

## Description

Remote working opportunities, access to sports and leisure activities, free access to public Paris city council's swimming pools, access to CROUS canteen, scientific campus in central Paris, professional development programs, well-being workshops, social benefits through CNAS, partial health insurance support, and 75% support for sustainable mobility.


## Related Videos

- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)
- [Rules, Heuristics, or LLMs? Lessons from Solving the Same Problem Twice](https://www.wearedevelopers.com/videos/100112-rules-heuristics-or-llms-lessons-from-solving-the-same-problem-twice)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [How Robots Learn to be Robots](https://www.wearedevelopers.com/videos/1632-how-robots-learn-to-be-robots)
- [On the straight and narrow path - How to get cars to drive themselves using reinforcement learning and trajectory optimization](https://www.wearedevelopers.com/videos/205-on-the-straight-and-narrow-path-how-to-get-cars-to-drive-themselves-using-reinforcement-learning-and-trajectory-optimization)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

## Related Articles

- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [The Fastest-Growing Tech Sectors to Look Out for in 2025](https://www.wearedevelopers.com/magazine/373-the-fastest-growing-tech-sectors-to-look-out-for-in-2025)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Building AI Solutions with Rust and Docker](https://www.wearedevelopers.com/magazine/494-building-ai-solutions-with-rust-and-docker)