Bioscope
What You'll Do
Develop Graph-Enhanced LLM Systems:
Design and implement state-of-the-art architectures that combine Graph Neural Networks with Large Language Models to reason over complex networks of biomedical knowledge, literature, and clinical evidence
Build Clinical Knowledge Discovery Tools:
Create AI-powered systems that enable physicians to rapidly synthesize insights from scientific literature, clinical trials, guidelines, and emerging research—transforming weeks of literature review into minutes of intelligent querying
Scientific Knowledge Graph Construction:
Build and maintain dynamic knowledge graphs that capture relationships between medical concepts, research findings, clinical guidelines, drug interactions, and treatment protocols from millions of publications
Science of Science Applications:
Apply bibliometric analysis, citation networks, and knowledge evolution modeling to identify emerging research trends, contradictory findings, and knowledge gaps in medical literature
Multi-Document Reasoning:
Develop systems that perform sophisticated reasoning across thousands of research papers, synthesizing evidence, resolving contradictions, and providing confidence-weighted clinical insights
Production Systems:
Deploy robust, scalable AI systems that handle real-time queries over massive biomedical knowledge bases with appropriate attention to accuracy, citation traceability, and clinical safety
Technical Focus Areas
Graph LLM architectures and graph-augmented language models
Knowledge graph construction, embedding, and reasoning
Citation networks and bibliographic analysis
Multi-hop reasoning over document graphs
Retrieval-Augmented Generation (RAG) over scientific literature
Named Entity Recognition and relation extraction from biomedical text
Evidence synthesis and contradiction detection
Semantic search and question-answering over medical knowledge bases
Qualifications Required
Master's degree in Computer Science, Machine Learning, Computational Biology, Information Science, or related quantitative field (PhD preferred)
2+ years of hands‑on experience with LLMs and Knowledge Graphs
(e.g., Graph‑RAG, LLM‑based knowledge graph reasoning, or similar architectures)
Strong foundation in deep learning frameworks (PyTorch)
Experience with Large Language Models (transformers, fine‑tuning, RAG systems)
Proficiency in Python and modern ML development tools
Experience putting AI systems into production environments
Strong understanding of NLP, information retrieval, and knowledge representation
Ability to read and implement research papers
Excellent communication skills and ability to work collaboratively
Preferred
Experience with
graph databases .
Demonstrated experience in Science of Science or Knowledge Discovery (bibliometrics, citation analysis, research trend detection, or scholarly knowledge mining).
Publications in top‑tier ML/AI/IR conferences (NeurIPS, ICML, ACL, EMNLP, WWW, KDD, etc.)
Experience with biomedical literature databases (PubMed, MEDLINE, clinical trial registries)
Background in biomedical NLP or clinical informatics
Experience with vector databases and semantic search systems
Familiarity with clinical decision support tools or medical information retrieval
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Develop Graph-Enhanced LLM Systems:
Design and implement state-of-the-art architectures that combine Graph Neural Networks with Large Language Models to reason over complex networks of biomedical knowledge, literature, and clinical evidence
Build Clinical Knowledge Discovery Tools:
Create AI-powered systems that enable physicians to rapidly synthesize insights from scientific literature, clinical trials, guidelines, and emerging research—transforming weeks of literature review into minutes of intelligent querying
Scientific Knowledge Graph Construction:
Build and maintain dynamic knowledge graphs that capture relationships between medical concepts, research findings, clinical guidelines, drug interactions, and treatment protocols from millions of publications
Science of Science Applications:
Apply bibliometric analysis, citation networks, and knowledge evolution modeling to identify emerging research trends, contradictory findings, and knowledge gaps in medical literature
Multi-Document Reasoning:
Develop systems that perform sophisticated reasoning across thousands of research papers, synthesizing evidence, resolving contradictions, and providing confidence-weighted clinical insights
Production Systems:
Deploy robust, scalable AI systems that handle real-time queries over massive biomedical knowledge bases with appropriate attention to accuracy, citation traceability, and clinical safety
Technical Focus Areas
Graph LLM architectures and graph-augmented language models
Knowledge graph construction, embedding, and reasoning
Citation networks and bibliographic analysis
Multi-hop reasoning over document graphs
Retrieval-Augmented Generation (RAG) over scientific literature
Named Entity Recognition and relation extraction from biomedical text
Evidence synthesis and contradiction detection
Semantic search and question-answering over medical knowledge bases
Qualifications Required
Master's degree in Computer Science, Machine Learning, Computational Biology, Information Science, or related quantitative field (PhD preferred)
2+ years of hands‑on experience with LLMs and Knowledge Graphs
(e.g., Graph‑RAG, LLM‑based knowledge graph reasoning, or similar architectures)
Strong foundation in deep learning frameworks (PyTorch)
Experience with Large Language Models (transformers, fine‑tuning, RAG systems)
Proficiency in Python and modern ML development tools
Experience putting AI systems into production environments
Strong understanding of NLP, information retrieval, and knowledge representation
Ability to read and implement research papers
Excellent communication skills and ability to work collaboratively
Preferred
Experience with
graph databases .
Demonstrated experience in Science of Science or Knowledge Discovery (bibliometrics, citation analysis, research trend detection, or scholarly knowledge mining).
Publications in top‑tier ML/AI/IR conferences (NeurIPS, ICML, ACL, EMNLP, WWW, KDD, etc.)
Experience with biomedical literature databases (PubMed, MEDLINE, clinical trial registries)
Background in biomedical NLP or clinical informatics
Experience with vector databases and semantic search systems
Familiarity with clinical decision support tools or medical information retrieval
#J-18808-Ljbffr