Application Guideline & Important information
Important Note
This is an internal offer.
Applications are only possible if you are scientist at one of the Geo.X partner institutions (AWI, DLR, FU Berlin, GFZ, HU Berlin, MfN, PIK, TU Berlin, UP).
If your application does not clearly indicate one of these affiliations, your application will not be submitted and will be deleted from the review process without further notice.
Time commitment, tools, and practicalities
Workload (and why it looks like this)
Expected workload is approximately 2–4 hours per week.
This is deliberate: working effectively with AI is a craft. You need repetitions in your own context (trying workflows, evaluating outputs, documenting what you did, and refining). The programme is structured so that practice is feasible alongside to your research work. Over time, a well-designed AI practice can streamline recurring tasks without losing accuracy.
Tools and costs
We cannot provide a single “standard tool” for everyone, because institutions have different guidelines and access conditions. The courses are largely tool-agnostic: you can use the tools available to you or acquire access yourself, depending on what you want to do. As the landscape of generative AI tools available to academia in Germany is continually evolving, we will highlight them in our online session and through additional formats, e.g., a newsletter or similar.
Participation fee: 150 EUR, to be paid after acceptance.
Coding
No coding experience is required for Module 1. Some Python experience can be helpful for automation ideas and tasks. Advanced applications in Module 2 depend on your project focus.
In-person events
In-person events will take place in Berlin or Potsdam and are expected to run from 09:30 to 16:30. We strongly encourage you to attend all in-person sessions to get the most out of the programme. If unavoidable conflicts arise, we will share the key materials and takeaways.
Community check-ins
Community check-ins are regular peer-exchange sessions designed to support collaboration and reflection. Participants discuss their progress, share use cases, explore tools, exchange feedback, and present what they have developed.
Certification
To receive the certificate, you should attend at least 75% of the in-person sessions (exceptions apply for illness or comparable circumstances) and complete a subgroup project output.
FAQ
Can I do this next to my PhD/postdoc workload?
We designed the course as a 10-month commitment so it can run next to your work. At the same time, this is a new skill to learn, which requires practice. There is no magical tool, especially as each person has different needs and tasks that require their own workflows and ideas.
What level of AI knowledge should participants have?
The programme is designed for researchers who are already experimenting and using with generative AI tools but have not yet systematically integrated them into their research workflow. To meet your requirements, we will also have a survey on your knowledge.
The focus is therefore is on developing structured practices: understanding where AI can meaningfully support the research lifecycle, improving prompting and workflow design, critically evaluating outputs, and building responsible and reproducible AI-supported routines. Module 1 covers core foundations (e.g., prompting strategies, building a knowledge base, evaluation habits, automation, and ethical/legal aspects), but it is not a sequence of beginner tutorials. It is built as applied and reflective practice with peer exchange. Participants are expected to adapt what they learn to their own research context and gradually develop a personal AI integration roadmap. In the second half of the programme, the emphasis shifts further toward domain-specific applications and participant-driven outputs closely connected to participants’ own research projects.
Do I need coding experience?
No coding experience is required for Module 1. Some Python experience can be helpful for automation ideas and tasks. Module 2 depends on your project focus.
Do we start with automation and AI agents right away?
No. We don’t start with automation or AI agents right away. We first build practical competencies for working with generative AI, focus on what works for you, and then you can move step by step towards automation.
Which AI tools will we use?
We don’t prescribe a single tool. Geo.X partner institutions have different rules and access permissions. The course is largely tool-agnostic: you can use the tools available to you or choose others based on your needs.
Will I need to pay for tools?
Potentially, depending on what you already have access to and what you want to do. Some workflows may only be possible via paid tools or paid tiers. We cannot cover these costs; participants use what is available through their institution or what they choose to access individually. As the landscape of generative AI tools available to academia in Germany continues to evolve, we will highlight them in our online session and newsletter.
What is the time commitment?
On average, approximately 2–4 hours per week.
What counts as a “project output”?
In Module 2, you work in a subgroup (or individual track) to produce at least one documented output connected to your research interests. This could, for example, be a workflow case study, a teaching module/OER, a white paper, or a best practices example.
Is this programme for software developers or AI engineers?
This programme is for researchers who want to integrate AI into their scientific work. If your main goal is programming-heavy AI engineering (e.g., building models, developer tooling), a specialized technical program is likely a better option for you.
Do you expect participants to share what they learn?
Yes, we want people to talk about AI. We encourage fellows to hold a small informal event in their working group or institute to share what was useful in the programme, what can be done, and what to watch out for. This, of course, is up to you, and we support you in doing so if you wish.