Purpose
Build practical familiarity with AI concepts relevant to biology while practicing careful technical writing, source citation, and clear communication for a professional portfolio.
Program Overview
This page documents the public structure of an unpaid educational research internship hosted by PraxAgent. The primary goal is learning: guided reading, research exposure, responsible AI use, technical communication, and a credible public body of work.
Mentorship
The internship is structured around independent learning with weekly remote mentorship discussions, draft review, research guidance, and feedback on clarity, accuracy, limitations, and presentation.
Boundaries
The internship is educational and portfolio-focused. It is not client work, customer work, employment, paid contractor work, production engineering, sales, or operational business support.
Learning Objectives
The program emphasizes useful foundations for AI-assisted biology research while keeping uncertainty, limitations, and publication quality front and center.
AI Foundations
- Language models, embeddings, retrieval-augmented generation, and model evaluation.
- Data quality, limitations, hallucination risk, and responsible use of AI assistance.
Biology Context
- Introductory exposure to bioinformatics, genomics, protein modeling, and biomedical retrieval.
- Reading, summarizing, and critiquing public papers and technical resources.
Technical Communication
- Accessible explainers, literature notes, diagrams, notebooks, and project summaries.
- Careful sourcing, limitation statements, and professional portfolio presentation.
Portfolio Artifacts
Portfolio artifacts are educational targets, not business deliverables. The exact set may change as interests, available time, and mentor feedback shape the research path.
Technical Posts
Three to five public technical posts, literature reviews, or research notes explaining selected AI + biology topics for a professional audience.
Demos or Diagrams
One or two lightweight educational demos, notebooks, diagrams, or prototype artifacts using public resources, toy examples, or synthetic data where feasible.
Final Summary
A final public summary describing topics studied, skills developed, artifacts completed, lessons learned, and possible next steps.
Authorship: Portfolio work is intended to be credited to the participating
intern. PraxAgent may host, format, archive, and link to the work to document the educational
internship and support professional development.
Initial Learning Plan
The plan is intentionally flexible. It can be adjusted as interests, skills, and project ideas develop.
Phase 1
Orientation
Set expectations for citations, responsible AI use, public writing, and safe handling of data.
Phase 2
Reading and Topic Selection
Read introductory resources or papers related to AI for biology, bioinformatics, protein models, genomics, biomedical retrieval, or scientific literature analysis.
Phase 3
First Publication
Draft a technical explainer or literature review, receive feedback, revise, and publish.
Phase 4
Educational Demo
Explore a small notebook, diagram, or prototype using public resources, toy data, or synthetic/example data.
Phase 5
Portfolio Development
Publish additional artifacts and improve clarity, citations, diagrams, limitation statements, and presentation.
Phase 6
Final Summary
Prepare a portfolio summary, resume description, professional profile language, and optional reflection on lessons learned.
Example Project Topics
Potential topics include literature review workflows, biological knowledge retrieval, model limitations, and responsible communication of AI-assisted research.
How language models can assist literature review in biology.
Retrieval-augmented generation for biological knowledge bases.
Limitations of LLMs in scientific reasoning.
Introduction to protein language models.
AI-assisted annotation of biological concepts.
Comparing embeddings for scientific abstracts.
A small question-answering demo over public biology papers.
Hallucination risk in AI-generated scientific summaries.
How to evaluate AI tools used for biology research support.
Ethical and practical issues in AI-assisted biomedical workflows.
Publication Standards
Public artifacts should be useful, careful, and honest about uncertainty. The page is designed as a job-search portfolio archive, not medical advice or business output.
Before Publishing
- Factual claims are sourced, cited, or qualified.
- Papers, datasets, images, code, and external resources are attributed.
- Meaningful AI assistance is disclosed where appropriate.
- Limitations, assumptions, and uncertainties are stated.
Excluded Content
- No medical advice, diagnosis, or treatment recommendations.
- No private, sensitive, proprietary, regulated, or restricted data.
- No confidential business projects or operational PraxAgent work.
- No client, customer, sales, administrative, or required marketing work.
Status: This page is the public program overview. Portfolio artifacts can be
added here as they are completed, reviewed, and ready for publication.