Analytic Learning Algorithm Researcher (Remote)

About the role

We are seeking a highly qualified researcher to advance analytic learning algorithms based on modular probabilistic architectures. The role blends deep theoretical work with practical implementation, targeting high‑impact domains such as finance, scientific discovery, and quantitative analysis. The position is fully remote, collaborating with a distributed, CET‑aligned research team.

Key responsibilities

• Develop numerical and analytical models for learning systems built on modular probabilistic structures.
• Design, analyze, and prove properties of learning algorithms, ensuring both theoretical soundness and practical applicability.
• Translate concepts from academic literature into implementable models and system components.
• Produce clean, well‑documented code to support research experiments and production‑aligned implementations.
• Collaborate closely with other researchers to integrate insights across different areas of expertise.

Required profile

• Advanced degree (PhD preferred) in Mathematics, Computer Science, Statistics, or a related quantitative field.
• Strong background in mathematical analysis methods such as optimal transport, information geometry, or continuous optimization.
• Experience with probabilistic graphical models, factor graphs, or related frameworks.
• Familiarity with tractable density‑estimation techniques like normalizing flows, autoregressive models, or probabilistic circuits.
• Ability to bridge theoretical reasoning with practical code implementation.
• Excellent communication skills for effective collaboration in a distributed research environment.

Required skills

• Optimal transport
• Information geometry
• Continuous optimization
• Probabilistic graphical models
• Factor graphs
• Normalizing flows
• Autoregressive models
• Probabilistic circuits

What we offer

• Fully remote work within a globally distributed, research‑focused team.
• Opportunity to tackle cutting‑edge theoretical problems with direct real‑world applications.
• High autonomy in a flat, research‑driven environment.
• Exposure to interdisciplinary applications across finance, scientific research, and advanced quantitative analysis.