Analytic Learning Algorithm Research

Job description

Accountabilities: Develop numerical and analytical models for learning systems based on modular probabilistic architectures. Design and analyze learning algorithms, ensuring theoretical soundness and practical applicability. Prove properties of algorithms and validate findings through rigorous experimental evaluation. Translate concepts from academic literature into implementable models and system components. Contribute clean, well-documented code supporting research experiments and production-aligned implementations. Collaborate closely with other researchers to integrate insights across different areas of expertise and support shared objectives. Help advance system design by ensuring coherence between mathematical reasoning and software implementation. Requirements: Advanced degree in Mathematics, Computer Science, Statistics, or a related quantitative field (PhD or equivalent research experience strongly preferred). Strong background in mathematical analysis methods such as optimal transport, information geometry, or continuous optimization. Experience working with probabilistic graphical models, including factor graphs or related frameworks. Familiarity with tractable density estimation techniques such as normalizing flows, autoregressive models, or probabilistic circuits. Ability to bridge theoretical reasoning and practical implementation in code. Strong analytical thinking skills with a research-oriented mindset and attention to mathematical rigor. Excellent communication skills and ability to collaborate effectively in a distributed research environment. Benefits: Fully remote work setup within a globally distributed, research-focused team (CET-aligned collaboration). Opportunity to work on cutting-edge theoretical problems with direct real-world applications. High level of autonomy in a flat, research-driven environment. Exposure to interdisciplinary applications across finance, physics, and scientific modeling. Collaboration with experts in machine learning theory, probabilistic modeling, and applied mathematics. Strong focus on intellectual growth, research impact, and publication-quality work. Competitive compensation aligned with experience and expertise. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 This position is posted by Jobgether on behalf of a partner company. We are currently looking for an Analytic Learning Algorithm Research in France. This role sits at the intersection of machine learning theory, probabilistic modeling, and applied research, focusing on building systems that represent domain knowledge through modular probabilistic structures. You will contribute to the design of learning algorithms that ensure consistency, composability, and uncertainty propagation across complex model architectures. The work spans both theoretical exploration and practical implementation, with direct applications in areas such as finance, scientific discovery, and advanced quantitative analysis. You will collaborate with a highly technical, research-driven team working fully remotely across CET-aligned time zones. The environment is intellectually rigorous, fast-evolving, and deeply focused on bridging mathematical reasoning with real-world computational systems. This is an opportunity to contribute to foundational research with tangible impact across multiple high-stakes domains.