Topic: Generative AI

Generative AI is changing scholarly publishing. Editors of applied linguistics journals discuss its effects on publication practices and the challenges and opportunities facing their field. The facilitated panel brings together perspectives from RELC Journal, Applied Corpus Linguistics, and English for Specific Purposes to consider how scholarly publishing can respond to these changes.

Dan Meyer considers the questions generative AI raises for mathematics learning after four years of rapid technological development. He argues that its novelty has been accompanied by educational adoption and institutional change falling short of early expectations, including lower-than-anticipated teacher use. The session explores what the technology can do, what students need and what teaching entails, using these questions to examine AI’s contribution to education.

Investigate generative AI for engineering design, combining large language models with formal design grammars. Explore how models represent, reason about and generate designs with human collaborators, with applications in products, manufacturing and structures. The fully funded salaried doctorate includes teaching support in design, product development and CAD, and begins between February and September 2027. Apply through the ETH online recruitment portal. Application deadline: 2026-11-01.

Vincent Racaniello, Kathy Spindler and Brianne Barker discuss research using generative AI to design complete bacteriophage genomes, including reported viable phages active against antibiotic-resistant bacteria. They also examine a study linking recombinant shingles vaccination with lower cardiovascular-event risk. The discussion connects genome modelling, experimental virology and vaccine research, alongside listener letters and weekly selections.

Postdoctoral research advancing generative AI applied to biology, including developing frontier biological foundation models; requires a PhD in computer science or related fields and expertise in Python and deep learning frameworks.

Generative AI is fundamentally transforming the software development industry by improving processes such as software testing, bug detection, bug fixes, and developer productivity. This talk explores how AI-driven techniques, particularly large language models (LLMs), are being utilized to generate realistic test scenarios, automate bug detection and repair, and streamline development workflows. As these technologies evolve, they promise to improve software quality and efficiency significantly. The discussion will cover key methodologies, challenges, and the future impact of generative AI on t