Job title: Research Intern

Department: R&D team of Bioptimus

Location: Paris, France

About us

Bioptimus is building the universal AI foundation model in biology to drive advancements in scientific research and biotechnological innovation. Leveraging a team of world-class experts, employing state-of-the-art AI technologies and accessing unique proprietary data at scale, we aim to fuel breakthrough discoveries and accelerate innovations in biomedicine and beyond. For more information, please visit https://www.bioptimus.com.

About the role

As a Research Intern, you will have the opportunity to improve biomedical research using state-of-the-art machine learning algorithms. In particular, you will work with an interdisciplinary team with both machine learning and biology expertise to develop and evaluate foundation models.

In particular, you will:

• Contribute to the implementation of strategies to align foundation models across different biological modalities, including but not limited to genomics, proteomics, single-cell omics and histopathology.
• Conduct methodological research for the pre-training and alignment of biological foundation models.
• Conduct the evaluation of multimodal biological foundation models and analyse their performance on downstream tasks.

The position is based in Paris. Ideally, you can join the team starting from Q2, for 6 months.

About you

Requirements:

• You are enrolled in a master degree/PhD in Computer Science-related field (machine learning, applied mathematics, computer science, software engineering).
• Authorised to legally work in France.
• Excellent written and oral communication skills. In particular, fluent in English (spoken and written).
• Strong analytical skills.
• Excellent command of coding in Python, and excellent grasp of programming best practices.
• Passionate about the intersections of healthcare/biology and AI.
• Strong expertise with deep learning frameworks (PyTorch, Jax, etc.) : You should already master one of these frameworks and have implemented (large) neural networks in previous projects.
• Highly organised and meticulous about details; capable of tackling complex problems logically and effectively.

Preferred qualifications/bonus skills:

• Demonstrated practical expertise in representation learning/large language models/generative AI, including prior experience of implementing, training and/or deploying large neural networks.
• Participated in the preparation of scientific publications at top ML conferences (NeurIPS, ICLR, ICML, …) or high-impact ML journals (JMLR, TMLR, ...).
• Prior experience working with biological data.