We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.

We are looking for full-time researchers to contribute to the development and analysis of our learning algorithms. You will work on interesting theoretical problems with immediate applicability to implementation of our system.

Our team works fully remotely, and mostly within the CET timezone.

Useful experience

• Development of mathematical analysis methods, for example: optimal transport, information geometry, continuous optimization methods

• Analysis of probabilistic graphical models, including factor graphs

• Implementation of tractable density estimators (normalising flows, autoregressive density models, probabilistic circuits)

• Translation between equational reasoning and code implementation

• Mathematics, Computer Science, or Statistics advanced degree (with PhD or equivalent research experience)

Responsibilities

• Develop numerical-analytical models of learning in our system

• Connect our research to existing literature

• Prove properties of algorithms and design experiments to validate results empirically

• Leverage the expertise of other team members effectively

• Write clean and well documented code

• Help other team members to deliver on their goals

On our website you can find more about our team and work culture, as well as example tasks that share some insight into the type of things team members are working on.

What we do: https://planting.space/ 

Ways of work: https://planting.space/org/ 

Team culture and example tasks: https://planting.space/joinus/