Ramit Debnath

The Centre for Human-Inspired Artificial Intelligence (CHIA) at the University of Cambridge is inviting applications for a PhD position in environmental computational social sciences. The group, led by Assistant Professor Ramit Debnath, focuses on the development and application of computational social science methods, leveraging advances in natural language processing, large language models, network theory, machine learning, and behavioural science. This interdisciplinary research opportunity is ideal for candidates interested in pushing the boundaries of AI and its applications to environmental and social challenges. The successful applicant will join a vibrant research centre and work closely with leading experts in the field. Applicants should have a strong background in computational social science, computer science, artificial intelligence, or related disciplines, and experience with relevant methodologies is highly desirable. The position offers the chance to contribute to cutting-edge research at one of the world's leading universities. Funding details are not specified in the announcement. Interested candidates should review the group website and follow the application instructions provided.

PhD opportunities are open for 2027 entry at the Centre for Human-Inspired Artificial Intelligence (CHIA), University of Cambridge . The post highlights research in human-inspired AI and a specific project area in machine learning causal inference , with applications to climate impacts and society . The opportunity is connected to Cambridge faculty across multiple schools and to collaborative work with Prof. Michael Alvarez at the Linde Center for Science, Society, and Policy (LCSSP), Caltech . The poster, Ramit Debnath , says they will take 1–2 PhD students this year. The post is especially relevant for students with strong interests in data science , AI , causal inference , and interdisciplinary research linking technology and societal impact. Key requirements mentioned by the poster include strong data science skills, knowledge of ML causal inference, the ability to work independently, a solid publication profile, and systems thinking. Funding information : the post points to separate funding deadlines and specifically mentions Gates Cambridge with a US round deadline of 14 October 2026 . No stipend or tuition details are given in the post itself, so applicants should consult the linked funding pages and application information. Deadline : the main application deadline is 6 December 2026 . How to apply : use the application information link, review the funding deadlines, and check the group/project links if you want to work directly with Ramit Debnath. Prepare an application that clearly matches the AI, causal inference, and climate/society research focus.

Applications are open for the third cohort of PhD students at the Centre for Human-Inspired Artificial Intelligence (CHIA), University of Cambridge. The opportunity is centered on research in trustworthy AI, human-AI collaboration, contextual safety evaluations, and the deployment of AI and agents in high-stakes settings. The poster also notes that co-supervision arrangements can be made with supervisors from across the university, and lists potential primary supervisors including Anna Korhonen, Per Ola Kristensson, John Suckling, Jenny Gibson, Ramit Debnath, Hatice Gunes, Mateja Jamnik, Roi Reichart, Ariel Goldstein, and Umang Bhatt. This is a PhD opening for Michaelmas (Autumn) 2027 entry. The application deadline is 8 December 2026. For US applicants, the Gates Cambridge funding deadline is 14 October 2026. The post does not specify stipend, tuition coverage, or other funding details, but it does mention Gates Cambridge funding for eligible US applicants. Applicants should review the application and lab details in the comments and submit their PhD application by the stated deadline. Ideal candidates are those interested in AI research with a strong human-centered and safety-oriented focus, especially around trustworthy systems and real-world deployment in sensitive contexts.