Rachel Parkinson.

Rachel Parkinson

Lecturer, Queen Mary University of London
Schmidt AI in Science Fellow, University of Oxford

Rachel Parkinson is lecturer in computational ecotoxicology and neuroethology at Queen Mary University of London and a Schmidt AI in Science Fellow at the University of Oxford. Her research integrates neurophysiology, behavior and computational modeling to understand how environmental stressors affect insect sensory systems and pollinator health. She develops AI-driven tools to accelerate bioscience research, including equipment for high-throughput behavioral toxicology and a large language model pipeline for systematic reviews.

Parkinson earned her Ph.D. at the University of Saskatchewan, where she investigated how pesticides alter visual motion circuits and escape behaviors in locusts, under the supervision of Jack Gray. As a Grass Fellow at the Marine Biological Laboratory, she demonstrated pesticide-induced disruption of orientation behavior in honeybees. She later held a Royal Society Newton International Fellowship at the University of Oxford, studying taste perception in bees with Geraldine Wright.

From this contributor

Explore more from The Transmitter

Illustration of a binocular view of a hilly path leading towards a temple.

Amid funding uncertainty, graduate neuroscience programs in the U.S. adapt

The Transmitter surveyed 50 graduate program directors to learn how they’re faring. Securing labs for new students is one of the greatest concerns.

By Emily Singer
9 November 2026 | 15 min read
Illustration of a pillar in the shape of a question mark.

The changing nature of the neuroscience Ph.D.

The best science can only be sustained by a steady influx of early-stage scientists and the innovative ideas they bring, says Diane Lipscombe. She reflects on the loss of international students, the value of training grants, and the challenges that lie ahead for graduate education.

By Emily Singer
9 November 2026 | 7 min read
Illustration of marble bust with pixels taken out of it.

Rethinking neuroscience graduate education in the age of AI

What knowledge from traditional training protocols needs to be maintained, what can be discarded, and what new types of training should be introduced? Six neuroscientists weigh in.

By Mark Goldman
9 November 2026 | 17 min read