Prospective Students

Graduate Student Opportunities: AI Foundation Models for Organismal Biology

Fully Funded MSc and PhD Positions with Dr. Russell Dinnage
Department of Biological Sciences, University of Alberta

Start Date: January 2026 or September 2026 (flexible)

Funding: Competitive funding packages available through University of Alberta assistantships and fellowships. Additional funding opportunities available through external scholarships and awards.

Research Focus

The Dinnage Lab develops foundation models for organismal biology – artificial intelligence systems that learn from and help us understand living systems from molecules to ecosystems. Our work bridges computational innovation with fundamental biological questions, creating new approaches that integrate biological principles into AI architecture design.

Students will have opportunities to engage with three major research themes:

1. AI-Driven Simulation-Based Inference
Develop novel AI foundation models for population genomics and phylogenetic comparative methods using Prior-Data Fitted Networks (PFNs) and other cutting-edge approaches. Projects may involve creating systems that can infer demographic histories from genomic data or model complex trait evolution across phylogenies.

2. High-Throughput Phenomics
Work with museum collections and citizen science datasets to develop AI frameworks for automated phenotype extraction and analysis. Students can develop methods for creating "digital-first specimens", develop new approaches to large-scale morphological analysis, or leverage AI to connect different species' data sources using multimodal models, to create 'vector databases' for comprehensive biodiversity analysis.

3. AI-Biology Cross-Disciplinary Inquiries
Explore AI systems as model organisms for evolutionary research, develop biology-inspired explainable AI methods, or create in silico evolutionary simulations using AI models as representations of complex phenotypes. This cutting-edge work examines how evolutionary principles can inform AI development and vice versa.

Students may also propose projects outside these themes in the area of computational or quantitative organismal biology, or at the intersection between organismal biology and machine learning or artificial intelligence methods.

Qualifications

Minimum Requirements:

  • BSc in biology, ecology, evolution, computer science, statistics, mathematics, or related field
  • Strong interest in both organismal biology and computational methods
  • Admissible to University of Alberta graduate programs

Competitive Applicants Will Have:

  • 🖥️ Experience with programming in R, Python, or similar languages
  • 🧠 Background in machine learning, deep learning, or advanced statistical methods
  • 📊 Experience with large datasets or computational biology approaches
  • 📝 Strong written and oral communication skills
  • 🌱 Interest in interdisciplinary collaboration and open science practices

The Advisor

Dr. Russell Dinnage is an Assistant Professor whose research develops AI foundation models for understanding organismal biology. He works at the intersection of the fields of ecology, evolutionary biology, statistics and computer science. His work has been published in leading journals including Nature Ecology & Evolution, Science Advances, and Evolution. He currently serves as Associate Editor at Methods in Ecology and Evolution. His research program emphasizes ethical AI development, open science principles, and collaborative approaches to complex biological questions.

The Environment

The University of Alberta's Department of Biological Sciences offers world-class research facilities and a collaborative environment for interdisciplinary work. Students will have access to high-performance computing resources, extensive natural history collections, and opportunities to collaborate with the Alberta Machine Intelligence Institute (Amii). Edmonton provides an excellent quality of life with abundant outdoor recreation opportunities and a vibrant cultural scene.

Application Process

Applications are reviewed on a rolling basis. For full consideration for a Jan 2026 start, submit by July 15, 2025 (to allow time for application preparation before the University of Alberta deadline of August 1, 2025).

Before contacting me, please:

To officially apply, please send the following materials as a single PDF with subject line "Graduate Position - [Your Name]" to r.dinnage@gmail.com:

📋 Cover letter (max 1 page) describing your research interests, how they align with the lab's focus, and which theme(s) most excite you
📊 CV highlighting relevant research experience, programming skills, and publications/presentations
📝 Transcripts (unofficial acceptable for initial review)
🔬 Writing sample demonstrating your ability to communicate scientific concepts
📞 Contact information for 2-3 references (academic preferred)

I welcome applications from all qualified candidates and am committed to fostering an inclusive research environment that values diverse perspectives and backgrounds.

For questions about potential projects or the application process, feel free to reach out at r.dinnage@gmail.com or visit the lab website at https://rdinnager.github.io/dinnage_lab_website/

The University of Alberta, its buildings, labs and research stations are primarily located on the territory of the Néhiyaw (Cree), Niitsitapi (Blackfoot), Métis, Nakoda (Stoney), Dene, Haudenosaunee (Iroquois) and Anishinaabe (Ojibway/Saulteaux), lands that are now known as part of Treaties 6, 7 and 8 and homeland of the Métis. The University of Alberta respects the sovereignty, lands, histories, languages, knowledge systems and cultures of all First Nations, Métis and Inuit nations.

Mentoring Philosophy

My mentoring philosophy is grounded in the belief that exceptional computational biologists emerge when rigorous training meets supportive, inclusive guidance. Having worked across diverse institutional contexts—from a Hispanic-Serving Institution to international collaborations—I've learned that effective mentoring requires both high expectations and adaptive support systems.

Process-Based Development: I focus on helping mentees develop deep understanding of both computational methods and biological principles, rather than simply learning to apply tools. This means we spend time understanding why certain approaches work, when they fail, and how to critically evaluate results. I believe this foundation enables mentees to become independent thinkers who can tackle novel problems and adapt to rapidly evolving fields.

Bridging Multiple Worlds: The intersection of AI and biology requires scientists who can communicate across disciplinary boundaries. I help mentees develop fluency in both computational and biological thinking, encouraging them to see connections between abstract mathematical concepts and concrete biological processes. This includes regular practice presenting work to both computational and biological audiences, and developing projects that demonstrate genuine interdisciplinary integration.

Democratising Sophisticated Methods: I'm committed to making advanced computational approaches accessible to mentees from all backgrounds. This means providing multiple pathways to engage with complex material, offering structured support while maintaining high standards, and recognising that different learning styles and prior experiences require different approaches. I use continuous feedback and iterative improvement to ensure mentees build confidence alongside competence.

Fostering Scientific Independence: My goal is to develop mentees who become confident, independent researchers. This involves gradually increasing responsibility and decision-making autonomy, encouraging calculated risk-taking, and helping mentees learn from both successes and failures. I believe that learning to fail gracefully and persist through setbacks is essential for scientific careers, and I create environments where mentees can take intellectual risks without fear of judgement.

Inclusive Excellence: I recognise that diverse perspectives strengthen research and that systemic barriers have historically excluded many talented individuals from computational biology. I work actively to create inclusive environments where mentees from all backgrounds can thrive, providing flexible support structures while maintaining rigorous academic standards. This includes attention to both technical skill development and the professional skills needed for successful careers.

Work-Life Integration: Research careers are marathons, not sprints, and sustainable productivity requires healthy work-life integration. While I have high expectations for engagement and intellectual growth, I don't believe productivity should come at the cost of mental health or personal relationships. I help mentees develop realistic timelines, celebrate progress at all scales, and maintain perspective during inevitable challenging periods.

Collaborative Growth: I view mentoring as a mutualistic relationship where we both learn and grow. Mentees bring fresh perspectives, challenge assumptions, and often push the research in directions I wouldn't have considered alone. I'm committed to staying current with rapidly evolving fields and acknowledge when mentees develop expertise that exceeds my own in particular areas.

My approach adapts to each mentee's needs, career goals, and learning style, but always emphasizes the integration of computational sophistication with biological insight that defines our field. Whether mentees pursue academic careers, industry positions, or science communication roles, my goal is to prepare them as thoughtful scientists who can contribute meaningfully to addressing environmental and societal challenges.