Google DeepMind opens student researcher roles to support AI-driven cancer research
New PhD placements signal continued investment in AI for science, as Google DeepMind expands work on models targeting real-world biomedical challenges.
Google DeepMind is recruiting PhD students for short-term research roles focused on AI systems for cancer discovery, as the company continues to scale its work in applying large language models to biomedical research.
The positions, announced by Shekoofeh Azizi, Staff Research Scientist at Google DeepMind, in a LinkedIn post, will run for six to nine months starting between May and June 2026. The roles are open to PhD students in relevant technical fields and are primarily based in North America, with Mountain View listed as the preferred location.
Azizi said: “I'm hiring Student Researchers to join my team at Google DeepMind!”
She added: “We're building AI systems that accelerate scientific discovery in cancer! If that excites you, this might be your opportunity.”
Focus on AI for biomedical discovery
The hiring supports ongoing research into applying AI models to areas including therapeutics and single-cell biology. Azizi’s work focuses on developing large-scale systems designed to accelerate scientific discovery, with cancer research identified as a key area.
Her team is involved in several of Google’s medical AI initiatives, including the Med-PaLM series, Med-Gemini, and TxGemma, which are designed for healthcare and life science applications.
The student researcher roles form part of Google’s broader research program, which places PhD students into active projects across teams including Google DeepMind, Google Research, and Google Cloud.
Program structure and requirements
The positions are designed as flexible research placements, with durations and working arrangements varying depending on the project. Participation requires candidates to be enrolled in a PhD program in fields such as computer science, statistics, applied mathematics, or related disciplines.
Applicants are expected to demonstrate experience in areas including machine learning, natural language processing, or data science, alongside prior research activity such as published papers or lab work.
The program is non-conversion, meaning it is not intended to lead directly to full-time employment, and candidates must be able to work from the United States for the duration of the placement.
AI research pipelines extend into education and skills
The recruitment highlights how AI research pipelines continue to intersect with higher education, with PhD students contributing directly to the development of frontier systems.
For the education sector, this reflects a broader shift toward closer alignment between academic research and industry-led AI development, particularly in areas where access to data, infrastructure, and real-world applications shapes outcomes.
While positioned as research roles, the program also functions as a skills pathway, giving students experience in deploying AI systems at scale within industry environments.
