Research Assistant - Financial Language Models

Interested candidates are invited to submit their resume, transcripts, cover letter, and the names and contact information of two references who have directly supervised their work to [email protected] by August 21, 2026. Applications are reviewed on a rolling basis. ABOUT THE RISK AND INSURANCE STUDIES CENTRE (RISC) Our vision is for risk mastery to advance universal progress through interdisciplinary knowledge, innovative education, and dialogue. We aim for a Risk Management & Insurance (RMI) field grounded in translational, rigorous scientific research, attracting and nurturing top emerging talent, and collaborating with diverse stakeholders for a safer, more sustainable, and prosperous future. Our mission is to create and mobilize interdisciplinary knowledge to advance the field of Risk Management & Insurance and to develop and establish innovative education and training programs that empower individuals to navigate and master the complexities of risk. More specifically, with the world’s finest research experts on board and a unique place in the RMI industry, our overarching mission involves the creation of transformative scientific research and its timely translation into tangible, real-world applications within the RMI industry. This entails: • Conducting comprehensive and rigorous scientific research that addresses industry challenges and paves the way for better systemic risk management solutions. • Cultivating emerging talent to be ready to step into rewarding RMI jobs and become the risk leaders of tomorrow. • Working collaboratively with diverse stakeholders to bridge the gap between Academia, Industry, and Governments. This collaboration ensures that our initiatives contribute to a safer, more sustainable, and prosperous future for all Canadians. As we continue to expand, we are looking for a motivated candidate to join our team. The Opportunity This is a four-month paid research position with the possibility of developing into a publishable article. You will work directly with Dr. Ed Furman of RISC Foundation and York University and Dr. Walid Mnif and will have access to a collaborative network spanning industry and academia. The Problem Financial language models hallucinate. They generate confident-sounding answers that are factually wrong. Most existing work catches these errors after the fact. This project asks a harder question: can we predict that a model is about to hallucinate before it generates a single token? The Project You will work on a three-part system applied to Canadian bank filings and financial disclosure analysis, where a missed hallucination has real regulatory and liability consequences. Risk scoring: build a scorer that examines the incoming question, retrieved documents, and internal model signals to output a probability that the response will contain an error Error structure: investigate whether model mistakes follow patterns. Consistent confabulation of regulatory ratios or hallucinated dates under sparse context are not random failures -- they are structured, and can be characterized Statistical guarantees: wrap the system in a formal guarantee, that is for any flagging threshold, bound the fraction of hallucinations missed, with a proof that holds regardless of query distribution shift A secondary thread distinguishes between model failure and unanswerable questions, separating "the model got it wrong" from "the evidence was never there." What We Are Looking For We are looking for a strong researcher who sits at the intersection of statistics, mathematics, and computer science. Required: Graduate training -- Ph.D. level preferred -- in statistics, mathematics, computer science, or a closely related field Proficiency in Python and deep learning frameworks, e.g. PyTorch Solid understanding of neural network architectures including transformers Working knowledge of linear algebra and statistical testing An asset: Experience with model calibration Familiarity with conformal prediction or distribution-free uncertainty quantification Interest in financial applications or NLP Interested candidates are invited to submit their resume, transcripts, cover letter, and the names and contact information of two references who have directly supervised their work to [email protected] by August 21, 2026.

The researcher will develop a three-part system to predict and analyze hallucinations in financial language models using Canadian bank filings. This includes building a risk scorer, investigating error structures, and establishing statistical guarantees for the system.

Candidates should have graduate training, preferably at the PhD level, in statistics, mathematics, or computer science. Proficiency in Python, PyTorch, and a strong understanding of neural network architectures and linear algebra are required.

• Financial analysisPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• PyTorch
• Deep Learning
• Transformers
• Linear Algebra
• Statistical Testing
• Model Calibration
• Conformal Prediction
• Distribution-free Uncertainty Quantification
• Natural Language Processing
• Financial Analysis

• Science & Research
• Data & Analytics
• Software
• Finance & Accounting
• Technology

Minimum education

Master’s degree

Minimum experience

2+ years

Apply by

Sep 9, 2026

Posting language

English

Working hours

40 hours per week

Seniority

Entry level

Application method

Direct apply is available