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Postdoctoral Fellowship in Quantum-Enhanced LLM Reasoning for Biomedical Literature Discovery @Chennai

Amrita Vishwa Vidyapeetham, Chennai Campus is inviting applications for the post of Postdoctoral Fellowship in Quantum-Enhanced LLM Reasoning for Biomedical Literature Discovery

For details contact : g_bharathimohan@ch.amrita.edu

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Job Title Postdoctoral Fellowship in Quantum-Enhanced LLM Reasoning for Biomedical Literature Discovery
Required Number 1
Qualification Qualification - Ph.D. in Computer Science and Engineering, Machine Learning, Quantum Machine Learning, Natural Language Processing
Job Description

  • This position is part of an R&D 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.

  • The role will involve R&D 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.
Job Category Research
Last Date to Apply August 28, 2026

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