PhD in AI-driven digital phenotyping, genomics, and equine biomechanics Swedish University of Agricultural Sciences in Sweden

PhD position at the Swedish University of Agricultural Sciences (SLU), Uppsala, in AI-driven digital phenotyping and genomics, with a strong focus on computer science, machine learning, computer vision, genomics, biomechanics, and animal science.

The project, titled “The code that shapes the body: AI and digital phenotyping to decode evolutionary morphology in domesticated animals”, investigates how genetic variation shapes body form and function in horses. The work combines multimodal AI with large-scale biological datasets, including images, 3D body models, motion data, expert breeding assessments, and whole-genome sequences.

The PhD student will develop machine learning models for digital phenotyping and genomics, work with multimodal datasets, implement models in Python/PyTorch, and use high-performance computing infrastructure. Research directions include multimodal modelling, structure–function analysis between morphology and movement, and genome–phenotype modelling using machine learning and genomic language models.

Supervision is by Associate Professor Elin Hernlund (SLU), co-supervision by Professor Hedvig Kjellström (KTH Royal Institute of Technology), and Associate Professor Sofia Mikko (SLU). The project is conducted at the Department of Animal Biosciences, within the Animal Locomotion and Emotion Laboratory, and is part of the DDLS Research School. Collaborations include KTH and international partners in Germany and Italy.

Eligibility highlights: a Master’s degree or equivalent, relevant technology coursework, independent degree-project experience, English proficiency equivalent to Engelska B/6, and a background in programming/data science. Experience in machine learning, deep learning, computer vision, 3D data, genetics, or large datasets is especially valued.

Funding: doctoral employment for 4 years, full-time, with salary according to local collective agreement. Deadline: 1 June 2026. Start date: October 2026.

Apply via the SLU vacancy page and include the required application attachments before the deadline.