Conduct a literature review on AI/ML techniques used in chromatographic data analysis to gain foundational knowledge and guide project development and strategy.
Collaborate with cross-department teams to organize and preprocess chromatogram datasets for machine learning model development.
Develop and implement machine learning models that integrate existing Standard Operating
Procedures (SOPs) and expert knowledge to automate chromatographic peak integration.
Evaluate and validate model performance by comparing predictions against current benchmarks, ensuring accuracy and reliability.
Engage in exploratory data analysis to identify patterns and features that can inform model improvements.
Maintain thorough documentation of methodologies, experiments, and results to facilitate knowledge sharing and project continuity.
Prepare regular progress reports and presentations to communicate findings and insights to the project team and stakeholders.
Demonstrate initiative and a proactive approach to learning and problem-solving, seeking timely guidance when necessary.
Collaborate with cross-functional teams to integrate feedback and refine project deliverables.
Contribute to the development of automated workflows and tools that streamline chromatographic data analysis processes.
