Analytic Learning Algorithm Research
We are seeking full-time researchers to develop and analyze learning algorithms for a system that represents domain knowledge as modular probabilistic models. The role involves theoretical problems with immediate implementation applicability in finance and scientific research.
About the job
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
Skills
Optimal Transport, Information Geometry, Continuous Optimization, Probabilistic Graphical Models, Factor Graphs, Normalizing Flows, Autoregressive Density Models, Probabilistic Circuits
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