Scientific Work with AI

TitleTimeRoomTeacher
Scientific Work with AI29.05.2026 09:00 - 16:00 (Fri)TUM Graduate School: Boltzmannstraße 17, 85748 Garching bei München, Room 101Victoria Ruckerbauer & Michael Oberparleiter
Keywords: 
LLMs, Prompting Techniques, Generative AI, Agentic Coding, Data Analysis, AI Tools
Course Description: 

This workshop is designed for doctoral candidates who want to learn how to best use Large Language Models (LLMs) for scientific work, focusing on different aspects covered across three parts:

  • LLM Basics: This section covers the basics of LLMs, explaining how they operate and why they can hallucinate, as well as different aspects to consider when crafting prompts. We will also discuss different AI-powered tools, such as Copilot and Perplexity. 
  • Programming with AI: LLMs allow people with no to little coding experience to create helpful software scripts. Here, we want to introduce coding tools like OpenCode that integrate directly into your code base, reducing manual effort. 
  • Data Analysis with AI: Building on the previous section, we will focus on how to create data analysis and visualization scripts in Python using LLMs.

Each part of the workshop includes guided exercises. You are welcome to bring your own data for the data analysis part.

No prior AI or coding experience is required for this workshop.

Course aims: 
  • Use LLMs as productivity partners to accelerate scientific work, coding, and data analysis
  • Learn about when and how to use LLMs effectively
  • Gain hands-on experience writing and refining code with LLM assistance

Teaching methods: 

Presentations, Hands-on sessions, Interactive discussions.

This course fits doctoral candidates in the following phase: 

At the beginning of the doctorate │ during the doctorate │ at the end of the doctorate

Participation requirements: 

None. No prior experience with LLMs or coding is required.

Technical requirements: 

Bring your laptop. A running Python installation with a package manager (such as pip) is helpful, but setup support will be available during the workshop.

Course preparation: 
  • Reflect on your daily work as a doctoral candidate and identify tasks that involve the evaluation of large amounts of text or data. This is where LLMs can be most helpful.
  • If possible: Prepare a Python installation as described here.
Category: 
Fachübergreifende Veranstaltung
Event type: 
Seminar/Workshop
Organizer: 
Graduate School Geschäftsstelle
Responsibility for event: 
Hauptverantwortung
Format: 
In Präsenz
Course Language: 
EN
Course Capacity (Max): 
15
Course capacity (Min): 
1
Duration in hours: 
6
Financial contribution: 
20 Euro
Trainer: 
Victoria Ruckerbauer & Michael Oberparlaiter