Researchers across disciplines leverage these tools in various innovative ways. For instance, generative AI facilitates topic modelling on large datasets, automated thematic analysis, and classification tasks. It also aids in brainstorming new research ideas, summarizing papers and books, and even coding, which is particularly beneficial for researchers in non-technical fields. Furthermore, generative AI enhances data analysis and summarization, enabling researchers to upload a dataset and receive summary statistics, plots, and general insights, thereby streamlining the research process.
This course is tailored for researchers in all disciplines (both technical and non-technical) and at all levels, including research-oriented Master students, PhD students, postdocs, and assistant professors. The idea is that anybody who is eager to explore the transformative potential of Generative AI in their scientific studies is welcome in the course. Students will gain practical experience with cutting-edge generative AI models and techniques, such as GPT, DeepSeek, and Llama.
Students will learn to generate hypotheses, analyse complex datasets, and incorporate these tools into their own experiments. The course will also cover best practices, ethical considerations, and case studies from various scientific fields.
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