Research Scientist - Information Theory and Statistical Inference

What you'll do

• Develop methods to measure information, uncertainty, redundancy, information loss, and the value of additional evidence.
• Apply information theory, Bayesian inference, statistics, and probabilistic modeling to heterogeneous data, including its graph representations.
• Build algorithms, prototypes, benchmarks, and evaluation methods, and collaborate with research, engineering, and product teams to apply them.

What they ask for

• Ph.D. or thesis-based Master's degree in a listed or related quantitative field.
• Strong foundation in graph theory and in probability, statistics, information theory, or statistical inference.
• Research experience in at least one listed area, such as information theory, Bayesian inference, uncertainty quantification, or probabilistic modeling.
• Strong Python, MATLAB, or equivalent scientific computing skills; ability to turn mathematical concepts into computational methods and prototypes.

Nice to have

• Experience with information-theoretic methods, compressed sensing, Bayesian experimental design, or probabilistic graphical models.
• Experience with large-scale, noisy, heterogeneous, or partially observed data; research impact; or connecting theory to real-world decisions.

Tools

Python, MATLAB

AI focus

Develops information-aware AI methods and systems for reasoning and decision-making over enterprise data; familiarity with machine learning, representation learning, foundation models, or agentic AI is preferred. Deep prior experience with LLMs, RAG, prompt engineering, or agent frameworks is not required.