Postdoctoral Fellowship

Job Summary

This position is part of an RD team building hybrid quantum-classical frameworks that help LLMs reason more reliably over biomedical literature - extracting structured knowledge and generating testable hypotheses (e. g. , drug-target interactions, drug repurposing candidates, mutation disease associations) with clear provenance rather than unconstrained generation.

Responsibilities

• The role will involve RD focused on:
  • Quantum-enhanced extraction of knowledge graphs from biomedical literature, applying quantum graph neural networks to identify entities, relationships, and patterns across large corpora of biomedical publications.
  • Quantum semantic encoding of biomedical text papers, abstracts, and clinical literature to support downstream reasoning at a scale that addresses the rapid growth of unstructured biomedical data.
  • Hybrid quantum-classical retrieval-augmented reasoning pipelines for literature-grounded hypothesis generation in the medical domain, with explicit evidence provenance linking each generated hypothesis (e. g. , a candidate drug-target interaction) back to its supporting literature.
  • Evaluation of truthfulness, hallucination, and calibrated confidence in quantum-augmented biomedical hypothesis generation using automated methods cross-referencing generated drug-target hypotheses against curated interaction databases (DrugBank, ChEMBL, DGIdb) and retrospective temporal-split testing, combined with quantum-derived confidence/uncertainty scoring.
  • Development of a quantum-enhanced biomedical claim-verification module for scientific literature: automatically classifying each generated claim or hypothesis against retrieved evidence as Supported, Refuted, or Not Enough Information, extending established scientific claim-verification frameworks (e. g. , SciFact- and HealthVer-style benchmarks) with quantum semantic encoding for evidence-claim matching.

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