DONALD WLODKOWIC LAB

OPPORTUNITIES FOR PROSPECTIVE STUDENTS

We offer diverse multidisciplinary research opportunities at the intersection of biology, engineering, neuroscience, and computational analytics, suitable for:

• Biologists interested in animal behaviour, proto-cognition, minimal intelligence, comparative neuroscience, and evolutionary origins of adaptive behaviour

• Engineers interested in developing innovative technologies for behavioural phenotyping including programmable sensory environments, biomicrofluidic platforms, and automated experimental systems

• Computer scientists and data analysts interested in AI-enabled animal tracking, automated behavioural classification, computational ethology, and machine learning applications in biological research

Initially please reach out informally via e-mail to discuss potential projects. Please include a CV and a brief description of your research interests, specifically outlining how they align with our work.

Please note that Prof Wlodkowic has no control over the formal admissions process. All PhD and Masters candidates must apply via the competitive intake process at the RMIT School of Graduate Research.

Information on how to apply:

RMIT School of Graduate Research: https://www.rmit.edu.au/research/research-degrees 

To submit an Expression of Interest (EOI):

• Visit: https://www.rmit.edu.au/research/research-degrees/find-a-project 

• Search for "Prof Donald Wlodkowic"

• Select one of listed project that are currently available

• Apply online https://www.rmit.edu.au/research/research-degrees/how-to-apply

Should your EOI be successful, you will be invited to submit a full application and apply for available scholarships.

CURRENTLY AVAILABLE RESEARCH PROJECTS

Learning and Memory in Simple Organisms

Can invertebrates with simple central nervous sytems limited in their computational capacities learn to associate stimuli? How long do they retain such memories? This project investigates associative learning, habituation, and memory retention in invertebrates with minimal nervous systems. Using automated behavioral arenas, programmable sensory environments, and AI-enabled tracking, you will test whether simple organisms can form memories, how long those memories persist, and what neural or non-neural mechanisms support them. This work has implications for understanding the evolutionary origins of cognition and the minimal biological requirements for learning. 

Navigation Without a Complex Brain

How do organisms with minimal or absent nervous systems find shelter, avoid obstacles, and make adaptive spatial decisions? This project investigates goal-directed navigation in simple invertebrates using custom-designed mazes, barrier assays, and programmable spatial environments. You will test whether organisms like planarians, amphipods, or isopods can learn routes, remember spatial locations, or optimize paths — and what sensory cues (light, chemical gradients, tactile feedback) guide their decisions.

Sensory Preferences Across Species

What do simple organisms prefer, and why? This project maps baseline sensory behaviors — phototaxis, thermotaxis, chemotaxis, shelter-seeking — across diverse aquatic and terrestrial invertebrates. Using high-throughput automated tracking systems, you will quantify how these preferences change with context (feeding state, age, prior experience, population density) and whether organisms can learn new preferences through conditioning. This foundational work establishes the behavioral repertoire needed to ask deeper questions about proto-cognition. 

Comparative Cognition Across Invertebrate Phyla

Do different organisms solve the same problems differently? This project takes a comparative approach, testing multiple species (planarians, crustaceans, molluscs, insects) on standardized behavioral assays. You will ask: which proto-cognitive abilities are widespread vs. phylogenetically restricted? Does ecological niche predict cognitive style? What are the minimal neural substrates for different forms of learning? This work establishes broad patterns across the tree of life.

Pollutants and Proto-Cognition

Can environmental chemicals impair learning and decision-making in organisms we didn't realize could learn? This project investigates how pollutants, pharmaceuticals, and neuroactive compounds disrupt proto-cognitive processes in simple invertebrates. You will test whether exposed organisms show impaired habituation, associative conditioning, spatial memory, or adaptive decision-making — using behavioral ecotoxicology as an experimental tool to dissect mechanisms of basal intelligence.

AI-Powered Behavioral Tracking and Classification

How can we automatically detect learning, classify behavioral states, and quantify subtle changes in animal movement? This project focuses on developing machine learning tools for behavioral neuroscience. You will train AI systems to recognize phototaxis, chemotaxis, exploration patterns, freezing behaviors, and decision-making in small aquatic and terrestrial invertebrates. The goal is to create interpretable, reproducible methods for high-throughput behavioral phenotyping that can be applied across species and experimental contexts.