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 next to your research work. Over time, a well-designed AI practice can streamline recurring tasks without trading away rigor.
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 need acquire access yourself depending on what you want to do. As the landscape of generative AI tools available to the academia in Germany is consistently developing, we will highlight these in our online session and newsletter.
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 you have unavoidable conflicts, we will share the key materials and takeaways.
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 your own workflows and ideas. We join together to figure out what for each of us.
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, and that matters. The course is largely tool-agnostic: you can use the tools available to you, or choose to access others depending 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 the academia in Germany is consistently developing, we will highlight these 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 be for example, a workflow case study, a teaching module/OER, white paper or a best practices example.
Is this programme for software developers or AI engineers?
This programme is for researchers integrating AI into scientific work. If your main goal is programming-heavy AI engineering (e.g., building models, developer tooling), you’ll likely be better served by a dedicated technical programme.
Do you expect participants to share what they learn?
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, what you can do, and what to watch out for. This is of course up to you and we support you in doing this if you want to .