Evaluate and synthesize findings from biological, chemical, and clinical data sources.
Apply subject matter expertise to experimental design, medicinal chemistry research, and scientific data interpretation.
Review AI-generated outputs and assess their scientific accuracy, relevance, and reliability.
Identify errors, limitations, and opportunities for improvement in AI-generated scientific content.
Provide comprehensive written feedback and actionable recommendations to improve model performance.
Preferred Qualifications
Advanced degree, such as an MSc or PhD, in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related scientific discipline.
Strong understanding of medicinal chemistry concepts, including structure-activity relationships (SAR), molecular properties, and drug design principles.
Demonstrated experience handling, analyzing, and interpreting large-scale omics or cheminformatics datasets.
Familiarity with bioinformatics and cheminformatics tools, databases, and programming languages such as Python, R, RDKit, and KNIME.
Strong scientific communication skills, with the ability to explain complex research findings and technical concepts clearly.
A track record of contributing to research projects involving biology, chemistry, and data science.
Experience working with multidisciplinary research teams or in remote project environments is advantageous.
Core Skills
Bioinformatics and computational biology
Medicinal chemistry and drug discovery
Cheminformatics and molecular data analysis
Biological and chemical dataset curation and annotation
Omics data interpretation
Wet-lab research knowledge
Python and R
RDKit and KNIME
Scientific research and experimental design
AI-generated scientific content evaluation
Technical documentation and scientific communication
Candidates with a strong foundation in bioinformatics and medicinal chemistry, practical research experience, and an interest in advancing AI-assisted scientific discovery are encouraged to apply.
