Training course · Claude · Research & Science

Claude for Researchers

Claude as an AI assistant for knowledge work: projects, Artifacts, long documents, analysis and precise writing for teams.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Claude for Researchers” is a hands-on course for roles such as Doctoral candidates, Research associates and postdocs and Professors and research group leaders. Rather than learning Claude in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Research & Science.

Anthropic's Claude has become a go-to AI assistant for demanding knowledge work: it handles very long documents, reasons transparently and writes in a measured, easily steerable style. In this practical day you learn to exploit these strengths systematically. You analyse lengthy reports, contracts or policies, compare several sources and extract key findings with references to the exact passages. With Projects you store background knowledge and working instructions permanently, so Claude knows your terminology, templates and quality standards and you no longer start from scratch with every request.

We also show how to use Artifacts to produce standalone deliverables – structured documents, tables, charts or small interactive tools – and share them with your team. You analyse files with the built-in analysis capability, use research and connectors to company data, and put Claude to work as a critical reviewer of your own writing. A dedicated session covers Team and Enterprise plans, access management and data protection. By the end you have tested project set-ups and prompt patterns that you can apply straight away in your own function.

Academic work is shaped by literature reviews, data analysis, grant writing and publication pressure. Generative AI can help at every stage: navigating and summarising the literature, sharpening research questions, writing analysis code, polishing manuscripts and preparing funding applications. AI agents can automate recurring steps such as data cleaning or cross-checking references. At the same time, AI touches core questions of research integrity: who is an author, how is AI use disclosed, how do you prevent fabricated references? Researchers therefore need more than prompting tricks – they need a methodologically sound, transparent and reproducible way of working with AI tools.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Doctoral candidates
  • Research associates and postdocs
  • Professors and research group leaders
  • Research offices and grant managers
  • Corporate R&D teams

Typical challenges we address

  • An ever-growing body of literature that is hard to keep up with
  • Heavy writing load for proposals, reports and papers
  • Data analysis that requires programming skills
  • Uncertainty about AI rules set by journals, funders and universities

What you will be able to do

  • You write precise, structured instructions that let Claude handle complex tasks reliably.
  • You analyse long and multiple documents and obtain referenced, verifiable summaries.
  • You set up Projects with a knowledge base and style rules and share them with your team.
  • You use Artifacts and the analysis capability to produce usable documents, tables and evaluations.
  • Use AI for literature work and writing without breaching research integrity standards.
  • Produce traceable analysis code with AI support and document it reproducibly.
  • Disclose AI use transparently in line with journal, funder and institutional requirements.

Course outline

Module 1 Understanding and directing Claude
  • Model family, strengths and limitations compared with other assistants
  • Clear instructions with context, examples and the desired output format
  • Structured input using sections and tags for complex tasks
  • Extended thinking and step-by-step approaches for demanding questions
Module 2 Analysing long documents
  • Uploading and querying large PDFs, reports and contract sets
  • Comparing multiple documents and surfacing contradictions
  • Summaries with references to specific passages
  • Deriving checklists and review grids from policies and guidelines
Module 3 Projects and knowledge base
  • Creating Projects with knowledge documents and persistent instructions
  • Storing style and terminology rules for consistent results
  • Sharing Projects with the team and developing them together
  • Making good use of connectors to drives and business applications
Module 4 Artifacts, analysis and writing
  • Creating Artifacts for documents, tables, charts and interactive tools
  • Analysing files and spreadsheets with the built-in analysis capability
  • Drafting, shortening, rewording and tailoring text to audiences
  • Using Claude as a critical reviewer of arguments and gaps
Module 5 Team use, data protection and quality
  • Team and Enterprise plans: administration, roles and sharing
  • Handling confidential and personal data in line with GDPR
  • Checking output: verifying facts, figures and quotations
  • Defining internal guidelines and reusable prompt patterns
Module 6 Practice lab: Research & Science
  • Navigating the literature
  • Sharpening research design
  • Writing analysis code
  • Revising manuscripts
  • Preparing grant proposals
  • Analysing qualitative data
Module 7 Data protection, quality & transfer to daily work
  • Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Navigating the literature

AI summarises abstracts and full texts, extracts methods, samples and findings into comparison tables and helps formulate search strings for scholarly databases – every source is then verified in the original.

Sharpening research design

Research questions, hypotheses and operationalisations are stress-tested with AI as a critical sparring partner to spot gaps, confounders and alternative explanations early. Control conditions and limits to generalisability are discussed as well.

Writing analysis code

Researchers without deep programming skills use AI to produce Python or R scripts for data cleaning, statistics and visualisation, documenting them clearly to ensure reproducibility.

Revising manuscripts

AI supports language editing, structuring and shortening of academic texts, checks terminology for consistency and helps draft responses to reviewers without taking over authorship. Your argument stays your own.

Preparing grant proposals

Funding guidelines are turned into checklists, work packages and milestones are structured, and sections such as impact plans or lay summaries are drafted in several versions.

Analysing qualitative data

Interview transcripts and open-ended responses are pre-coded and grouped thematically with AI; researchers review, refine and document the coding frame according to established methodology. Inter-coder checks remain essential.

Prerequisites

Confident use of office applications and a web browser; prior AI experience is helpful but not required. Your own non-confidential documents are welcome as practice material. Experience of academic work in a discipline; programming skills are helpful but not required.

Technical requirements

A laptop with an up-to-date browser or the Claude desktop app and a Claude account; we recommend Pro, Team or Enterprise access for Projects and larger files.

Data protection & compliance

Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 Online Request
  • Fri, 11 December 2026 Stuttgart Request
  • Mon, 11 January 2027 Online Request
  • Wed, 20 January 2027 Berlin Request
  • Wed, 3 February 2027 Online Request
  • Mon, 8 February 2027 Munich Request

Frequently asked questions

How does Claude differ from ChatGPT in everyday work?

Both are capable general-purpose assistants. Claude particularly stands out with very long documents, careful reasoning and an easily steerable writing style, while Projects and Artifacts support structured work. Which tool fits better depends on your tasks, integrations and company policies – the course gives you criteria for an informed assessment.

What are Artifacts and why would I use them?

Artifacts are standalone deliverables that Claude creates in a separate panel next to the chat – a document, a table, a chart or a small interactive tool, for example. They can be revised in a targeted way, downloaded and shared. A conversation therefore produces ready-to-use results rather than scattered fragments of text.

Is my data used to train Claude?

On Team and Enterprise plans, business content is not used for model training as a matter of principle. For individual accounts this depends on settings you should review yourself. Regardless of the plan, GDPR and internal confidentiality rules apply. In the course we walk through the relevant settings and the questions to ask.

Can I use AI to write my PhD thesis?

That depends on your doctoral regulations and institutional policies. AI is often permitted for language editing and brainstorming if its use is disclosed and your own intellectual contribution remains clear. In the course you learn to document use properly and assess common grey areas with confidence.

How do I avoid fabricated references?

Language models can produce plausible-sounding citations that do not exist. Work with tools that query real databases, ask for DOIs and verify every source in the original. The course demonstrates a workflow that combines AI-assisted searching with reliable reference management.

How much does “Claude for Researchers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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