Dr. Elena Richter
AI Research Lead · TU Munich
Machine Learning · AI Systems
AI Training
AI for Scientific Research and Discovery
Accelerate literature review, hypothesis generation, and reproducible research workflows.
Overview
Research productivity is bottlenecked by information overload and fragmented tools. Learn how to use AI for systematic reviews, hypothesis exploration, code and data analysis assistance, and reproducible pipelines — while maintaining academic integrity and citation standards.
Curriculum
Week 1 — AI-Assisted Literature & Discovery
- Systematic review automation
- Citation integrity
- Research question framing
Week 2 — Data Analysis & Experiment Design
- Statistical assistance
- Hypothesis generation
- Reproducibility practices
Week 3 — Computational Workflows
- Notebook automation
- Code review with AI
- Pipeline documentation
Week 4 — Capstone: Research Acceleration Plan
- Personal research workflow
- Publication-ready outputs
- Ethics and attribution
Learning Outcomes
- Documented AI-augmented research workflow
- Literature synthesis artifact with verified sources
- Reproducibility checklist for your lab or team
- Verified AI Capability Certificate
Skills Covered
Research Automation
Literature Synthesis
Reproducible Science
Academic AI Ethics
Expert Instructors
Learn from verified industry practitioners and professors from the Praktix expert network.
Franziska Eva Ettenhuber
Doctoral Student · Ludwig Maximilian University Munich
Industry Expert
Karan Khurana
Professor /Researcher · Antwerp Management School, University of Antwerp
Industry Expert
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