Please send enquiries via email to oisin.macaodha (at) with the keyword phd-enquiry in the subject.

PhD: Automated teaching of visual expertise.

To make education more accessible and affordable we need to look to automated approaches, such as computer assisted teaching, for scalable solutions. This project explores the development of automated algorithms for teaching knowledge to human learners. Specifically it asks the question, “How can we efficiently teach large numbers of visual concepts to humans automatically?” To answer it, we will develop novel computational models of human knowledge acquisition along with efficient teaching algorithms designed for large scale deployment. As a motivating example, we tackle the problem of teaching human learners how to classify visually similar categories in large image collections e.g. teaching a doctor how to diagnose different pathologies in medical images or teaching a naturalist how to identify different species of plants and animals in online image collection.

Keywords: Machine learning, machine teaching, human learning, deep probabilistic models.
Apply: Please send enquiries via email to oisin.macaodha (at)

PhD: Machine learning in the wild – fine-grained learning from audio and visual data.

We are offering a PhD studentship as part of the Biome Health Research Project, related to the development of machine learning methods needed to analyse large image and acoustic datasets. Current state-of-the-art methods in machine learning for image and acoustic classification are based on supervised deep learning neural networks (DNNs), and require large amounts of carefully annotated training data. However, these annotations often need expert knowledge to recognise and label different animal species from images. As such, these methods are both time consuming and expensive to collect. The goal of this project is to investigate and develop next generation methods for training DNNs that are more efficient in how they utilise supervision. This will include methods for self-supervised representation learning, utilising additional available metadata, and multi-modal learning from paired audio and visual data. The student will be based at UCL’s Centre for Biodiversity and Environmental Research and the Department of Computer Science. In addition, the student will work with researchers at the School of Informatics at University of Edinburgh, and will have the opportunity to work collaboratively with other partners in the Biome Health Team which include WWF UK, Imperial College London and the Zoological Society of London.

Keywords: Deep learning, computer vision, self-supervised learning, multi-modal learning.
Apply: More information and details on how to apply can be found here (deadline 22 Nov 2019).

PhD: Human-Machine Collaboration for Efficient Spatio-Temporal Biodiversity Monitoring.

The goal of this project is to leverage the complementary strengths of humans and machines to develop probabilistic models and algorithms for efficient data collection across space and time. Human operators can help deploy robotic systems, place remote static sensors in the wild, and perform data collection themselves (e.g. with cameras). Taking inspiration from machine learning techniques such as active learning the aim is to adaptively estimate (1) where to sample and (2) what method to sample with, to obtain as complete as possible estimate of the true underlying distribution with significantly less sampling effort.

Keywords: Machine learning, computer vision, spatio-temporal distribution modeling, active learning, deep probabilistic models.
Apply: More information and details on how to apply can be found here (deadline 31 Jan 2020).

PhD: Deep Learning of Object Shape from Video.

The goal of this project is to develop novel deep networks that can reason about object shape using only video as supervision. By learning representations that disentangle shape and appearance the developed models should be able to perform fine-grained object classification with significantly fewer training examples.

Keywords: Computer vision, machine learning, monocular depth, fine-grained visual classification.
Apply: More information and details on how to apply can be found here (deadline 31 Jan 2020).