Spontaneous Application - Research Scientist (Core AI or AI for Science)

Indefinido
Paris
Unos días en casa
Salario: No especificado
Experiencia: > 5 años
Formación: Doctorado

Sigma Nova
Sigma Nova

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El puesto

Descripción del puesto

This is not a live opening, but part of our proactive pipeline building.
We’re looking to meet exceptional scientists ahead of potential hires in late 2025 or early 2026.

Applications will be reviewed starting in September.

This page outlines the types of research profiles we’re excited to meet, not a fully scoped role.

Sigma Nova, we’re building foundation models for science—starting with the brain, and expanding into new domains like biology, climate, and physics.

To do that, we are always on the lookout for deep research talent, typically falling into one of two archetypes.



Requisitos

Profile 1 — Core AI Researchers

You’re a deep learning scientist working on core generative modelling, RL, representation learning, or scalable architectures. You enjoy designing new training objectives, pushing the limits of transformer variants, or working with complex temporal or multimodal data. You likely have a strong publication record and experience developing new model components that have been adopted in real systems or research stacks.

Your past work might include:

  • Diffusion models, score-based generative methods, or GANs

  • Foundation model design (attention mechanisms, tokenisation, scaling laws)

  • Long-context or irregular time series modelling (e.g., EEG, spatiotemporal data)

  • Reinforcement learning or decision-making frameworks


Profile 2 — AI for Science Researchers

You’re a scientist who has integrated modern AI into a scientific domain—be it physics, chemistry, climate, neuroscience, or another field. You’ve either built domain-specific deep learning models or adapted frontier architectures to simulate or explain natural phenomena. You’re fluent in both theory and application, and you’ve published in scientific or AI journals.

Your background might include:

  • Molecular modelling, simulation surrogates, physics-informed ML, or brain modelling

  • Training GNNs, transformers, or diffusion models on scientific data

  • Scientific computation with a data-centric, generative, or learning-based twist

  • Collaborations in academia, specialised startups, or big tech research teams


Proceso de selección

We’re currently not actively hiring, but want to engage with researchers who see themselves thriving in this kind of environment. Our next wave of hires may begin in early 2026, with some flexibility for exceptional fits.

Applications will be reviewed starting in September.

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