[Biopython] [Training Courses] AI-Assisted Coding for Bioinformatics with Python & Agentic AI for Life Sciences (Online, July 2026)

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=0A =0ADear all,=0A=0AWe would like to announce two upcoming online trainin=
g courses from Physalia Courses focused on the practical use of Artificial =
Intelligence in bioinformatics and life sciences research.=0A=0A1. AI-Assis=
ted Coding for Bioinformatics with Python - [ https://www.physalia-courses.=
org/courses-workshops/ai-powered-python/ ]( https://www.physalia-courses.or=
g/courses-workshops/ai-powered-python/ ) =0A 1=E2=80=932 July (Online)=0A=
=0AThis hands-on course introduces researchers to AI-assisted coding workfl=
ows and demonstrates how AI tools can be used effectively for Python progra=
mming in bioinformatics. Participants will learn prompt engineering, code e=
valuation, debugging strategies, reproducible documentation, and best pract=
ices for transparent AI-assisted programming.=0A=0ATopics include:=0A=0A* P=
rompt engineering for coding tasks=0A* Generating, debugging, and improving=
 Python scripts with AI=0A* Data wrangling and analysis using pandas=0A* Ev=
aluating AI-generated code for correctness and efficiency=0A* Reproducibili=
ty and documentation of AI-assisted workflows=0A* Practical genomics and bi=
oinformatics coding exercises=0A=0AThe course is intended for biologists an=
d bioinformaticians interested in increasing coding productivity while main=
taining scientific rigor and reproducibility.=0A=0A=0A=0A2. Agentic AI for =
Life Sciences - [ https://www.physalia-courses.org/courses-workshops/agenti=
c-ai/ ]( https://www.physalia-courses.org/courses-workshops/agentic-ai/ ) =
=0A 6=E2=80=937 July (Online)=0A=0AAgentic 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 p=
rovides a practical introduction to these emerging tools and teaches resear=
chers how to use them effectively and responsibly.=0A=0ATopics include:=0A=
=0A* Understanding the agent=E2=80=93provider=E2=80=93model ecosystem=0A* C=
ontext engineering and advanced prompting=0A* AI-assisted data analysis and=
 figure generation=0A* Automated presentation creation=0A* Building interac=
tive omics data portals=0A* Retrieval-Augmented Generation (RAG) and Model =
Context Protocol (MCP)=0A* Reusable AI skills, agent configuration, and res=
earch workflows=0A* Reproducibility, governance, and responsible AI use in =
research=0A=0AParticipants will work with real life-science datasets and le=
arn how to integrate agentic AI into their daily research workflows.=0A=0A=
=0ABoth courses are designed for life scientists, bioinformaticians, and da=
ta scientists who want practical, research-focused AI skills that can be ap=
plied immediately in their work.=0A=0A=0AWe would be grateful if you could =
share these opportunities with interested colleagues and students.=0A=0ABes=
t regards,=0A=0ACarlo=0A =0A--------------------=0A=0ACarlo Pecoraro, Ph.D=
=0A=0A=0APhysalia-courses [email protected]=0A=0Amobi=
le: +49 17645230846=0A=0A[ Bluesky ]( https://bsky.app/profile/physaliacour=
ses.bsky.social ) [ Linkedin ]( https://www.linkedin.com/in/physalia-course=
s-a64418127/ )=0A 
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<font face=3D"georgia" size=3D"2"><p style=3D"margin:0;padding:0;font-famil=
y: georgia; font-size: 10pt; overflow-wrap: break-word;">&nbsp;</p>=0A<p st=
yle=3D"margin:0;padding:0;margin: 0; padding: 0; font-family: georgia; font=
-size: 10pt; overflow-wrap: break-word;">Dear all,<br /><br />We would like=
 to announce two upcoming online training courses from Physalia Courses foc=
used on the practical use of Artificial Intelligence in bioinformatics and =
life sciences research.<br /><br />1. AI-Assisted Coding for Bioinformatics=
 with Python -&nbsp;<a title=3D"This external link will open in a new windo=
w" rel=3D"noopener noreferrer" href=3D"https://www.physalia-courses.org/cou=
rses-workshops/ai-powered-python/" target=3D"_blank">https://www.physalia-c=
ourses.org/courses-workshops/ai-powered-python/</a>&nbsp;<br />&nbsp;1=E2=
=80=932 July (Online)<br /><br />This hands-on course introduces researcher=
s to AI-assisted coding workflows and demonstrates how AI tools can be used=
 effectively for Python programming in bioinformatics. Participants will le=
arn prompt engineering, code evaluation, debugging strategies, reproducible=
 documentation, and best practices for transparent AI-assisted programming.=
<br /><br />Topics include:<br /><br />* Prompt engineering for coding task=
s<br />* Generating, debugging, and improving Python scripts with AI<br />*=
 Data wrangling and analysis using pandas<br />* Evaluating AI-generated co=
de for correctness and efficiency<br />* Reproducibility and documentation =
of AI-assisted workflows<br />* Practical genomics and bioinformatics codin=
g exercises<br /><br />The course is intended for biologists and bioinforma=
ticians interested in increasing coding productivity while maintaining scie=
ntific rigor and reproducibility.<br /><br /><br /><br />2. Agentic AI for =
Life Sciences -&nbsp;<a title=3D"This external link will open in a new wind=
ow" rel=3D"noopener noreferrer" href=3D"https://www.physalia-courses.org/co=
urses-workshops/agentic-ai/" target=3D"_blank">https://www.physalia-courses=
.org/courses-workshops/agentic-ai/</a>&nbsp;<br /> 6=E2=80=937 July (Online=
)<br /><br />Agentic AI systems can do much more than answer questions: the=
y can read files, write code, execute commands, build applications, and per=
form complex multi-step research tasks. This course provides a practical in=
troduction to these emerging tools and teaches researchers how to use them =
effectively and responsibly.<br /><br />Topics include:<br /><br />* Unders=
tanding the agent=E2=80=93provider=E2=80=93model ecosystem<br />* Context e=
ngineering and advanced prompting<br />* AI-assisted data analysis and figu=
re generation<br />* Automated presentation creation<br />* Building intera=
ctive omics data portals<br />* Retrieval-Augmented Generation (RAG) and Mo=
del Context Protocol (MCP)<br />* Reusable AI skills, agent configuration, =
and research workflows<br />* Reproducibility, governance, and responsible =
AI use in research<br /><br />Participants will work with real life-science=
 datasets and learn how to integrate agentic AI into their daily research w=
orkflows.<br /><br /><br />Both courses are designed for life scientists, b=
ioinformaticians, and data scientists who want practical, research-focused =
AI skills that can be applied immediately in their work.<br /><br /><br />W=
e would be grateful if you could share these opportunities with interested =
colleagues and students.<br /><br />Best regards,<br /><br />Carlo</p>=0A<p=
 style=3D"margin:0;padding:0;font-family: georgia; font-size: 10pt; overflo=
w-wrap: break-word;">&nbsp;</p>=0A<p style=3D"margin:0;padding:0;margin: 0;=
 padding: 0; font-family: trebuchet ms; font-size: 10pt; overflow-wrap: bre=
ak-word;">--------------------<br /><br />Carlo Pecoraro, Ph.D<br /><br /><=
br />Physalia-courses DIRECTOR<br /><br />[email protected]<br /><b=
r />mobile: +49 17645230846<br /><br /><a title=3D"This external link will =
open in a new window" rel=3D"noopener noreferrer" href=3D"https://bsky.app/=
profile/physaliacourses.bsky.social" target=3D"_blank">Bluesky</a>&nbsp;<a =
title=3D"This external link will open in a new window" rel=3D"noopener nore=
ferrer" href=3D"https://www.linkedin.com/in/physalia-courses-a64418127/" ta=
rget=3D"_blank">Linkedin</a></p>=0A<p style=3D"margin:0;padding:0;font-fami=
ly: georgia; font-size: 10pt; overflow-wrap: break-word;">&nbsp;</p></font>
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