Dear all,
We would like to announce two upcoming online training courses from Physalia Courses focused on the practical use of Artificial Intelligence in bioinformatics and life sciences research.
1. AI-Assisted Coding for Bioinformatics with Python - https://www.physalia-courses.org/cou...owered-python/
1–2 July (Online)
This hands-on course introduces researchers to AI-assisted coding workflows and demonstrates how AI tools can be used effectively for Python programming in bioinformatics. Participants will learn prompt engineering, code evaluation, debugging strategies, reproducible documentation, and best practices for transparent AI-assisted programming.
Topics include:
* Prompt engineering for coding tasks
* Generating, debugging, and improving Python scripts with AI
* Data wrangling and analysis using pandas
* Evaluating AI-generated code for correctness and efficiency
* Reproducibility and documentation of AI-assisted workflows
* Practical genomics and bioinformatics coding exercises
The course is intended for biologists and bioinformaticians interested in increasing coding productivity while maintaining scientific rigor and reproducibility.
2. Agentic AI for Life Sciences - https://www.physalia-courses.org/cou...ps/agentic-ai/
6–7 July (Online)
Agentic AI systems can do much more than answer questions: they can read files, write code, execute commands, build applications, and perform complex multi-step research tasks. This course provides a practical introduction to these emerging tools and teaches researchers how to use them effectively and responsibly.
Topics include:
* Understanding the agent–provider–model ecosystem
* Context engineering and advanced prompting
* AI-assisted data analysis and figure generation
* Automated presentation creation
* Building interactive omics data portals
* Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP)
* Reusable AI skills, agent configuration, and research workflows
* Reproducibility, governance, and responsible AI use in research
Participants will work with real life-science datasets and learn how to integrate agentic AI into their daily research workflows.
Both courses are designed for life scientists, bioinformaticians, and data scientists who want practical, research-focused AI skills that can be applied immediately in their work.
We would be grateful if you could share these opportunities with interested colleagues and students.
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]
We would like to announce two upcoming online training courses from Physalia Courses focused on the practical use of Artificial Intelligence in bioinformatics and life sciences research.
1. AI-Assisted Coding for Bioinformatics with Python - https://www.physalia-courses.org/cou...owered-python/
1–2 July (Online)
This hands-on course introduces researchers to AI-assisted coding workflows and demonstrates how AI tools can be used effectively for Python programming in bioinformatics. Participants will learn prompt engineering, code evaluation, debugging strategies, reproducible documentation, and best practices for transparent AI-assisted programming.
Topics include:
* Prompt engineering for coding tasks
* Generating, debugging, and improving Python scripts with AI
* Data wrangling and analysis using pandas
* Evaluating AI-generated code for correctness and efficiency
* Reproducibility and documentation of AI-assisted workflows
* Practical genomics and bioinformatics coding exercises
The course is intended for biologists and bioinformaticians interested in increasing coding productivity while maintaining scientific rigor and reproducibility.
2. Agentic AI for Life Sciences - https://www.physalia-courses.org/cou...ps/agentic-ai/
6–7 July (Online)
Agentic AI systems can do much more than answer questions: they can read files, write code, execute commands, build applications, and perform complex multi-step research tasks. This course provides a practical introduction to these emerging tools and teaches researchers how to use them effectively and responsibly.
Topics include:
* Understanding the agent–provider–model ecosystem
* Context engineering and advanced prompting
* AI-assisted data analysis and figure generation
* Automated presentation creation
* Building interactive omics data portals
* Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP)
* Reusable AI skills, agent configuration, and research workflows
* Reproducibility, governance, and responsible AI use in research
Participants will work with real life-science datasets and learn how to integrate agentic AI into their daily research workflows.
Both courses are designed for life scientists, bioinformaticians, and data scientists who want practical, research-focused AI skills that can be applied immediately in their work.
We would be grateful if you could share these opportunities with interested colleagues and students.
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]