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1–2 jobs· newest first

Research Scientist – Foundation Models and Multimodal AINew
Owkin · Paris, France+3 more
Hybrid
2027 Summer Intern - Translational Safety (Computational Toxicology)
Genentech · South San Francisco, US
$41–$52On-site · Internship · Intern

+10K new in the last hour

Research Scientist – Foundation Models and Multimodal AI

Owkin · Posted Nov 13, 2026

Paris, Île-de-France, France +3 more
Hybrid
2–4 years

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AI

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Lead research on foundation models and multimodal AI to advance pharmaceutical research.

Locations (4)

Paris, Île-de-France, France
France
United Kingdom
Germany

Responsibilities

Adapt and fine‑tune state‑of‑the‑art foundation models
Design and implement cross‑modal alignment architectures
Build benchmarking frameworks and evaluation pipelines
Collaborate with cross‑functional teams to integrate models into production
Identify high‑impact improvements in data preprocessing and embedding

Requirements

Ph.D. in Computer Science, Machine Learning, Mathematics or related quantitative field
Master’s degree in Computer Science, Machine Learning, Mathematics or related quantitative field
Nice to have: Biological or omics data experience
Nice to have: Docker
Nice to have: Kubernetes

Skills

Python PyTorch Hugging Face ecosystem Fine‑tuning techniques Foundation model adaptation Multimodal learning Cross‑modal representation learning CLIP‑style architectures Vision‑language models Data engineering tools Parquet AnnData HDF5 Hugging Face Docker

Benefits

Flexible work organization
Friendly and informal working environment
International team

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2027 Summer Intern - Translational Safety (Computational Toxicology)

Genentech · Posted Nov 10, 2026

South San Francisco, California, United States
On-site
Internship · Intern
$41–$52 per hour

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Develop and apply computational approaches to biological questions in target safety research.

Compensation

The expected salary range for this position based on the primary location of California is $41.00-$52.00 hourly.

Responsibilities

Develop reproducible computational workflows to integrate and analyze single-cell transcriptomic and multi-omics datasets.
Characterize in vitro models and assess their biological relevance through quantitative analyses and comparisons with reference datasets.
Explore and benchmark AI/ML methods, including foundation models, for analyzing high-dimensional biological data.
Collaborate with computational and experimental scientists to interpret findings and contribute to reusable analysis tools, including LLM-based or agentic workflows.

Requirements

Master's Degree
PhD
Nice to have: single-cell RNA-seq
Nice to have: Scanpy
Nice to have: AnnData

Skills

Python R bioinformatics data analysis version control

Benefits

paid holiday time off
12-weeks full-time paid internship
stipend based on location

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