Dr Jennifer Hoyal Cuthill

School of Life Sciences
Dr Jennifer Hoyal Cuthill



  • PhD University of Cambridge, (2011)

  • MSc Palaeobiology University of Bristol, (2007)

  • BSc Zoology University of Bristol, (2005)


University of Essex

  • Postdoctoral Research Fellow, Institute for Analytics and Data Science and School of Life Sciences (1/10/2019 - 30/9/2022)

  • Lecturer, School of Life Sciences, University of Essex (1/10/2022 - present)

Other academic

  • Affiliate Researcher, Tokyo Institute of Technology, Earth Life Science Institute (1/3/2018 - present)

Research and professional activities

Research interests

Biological machine learning

Developing and applying machine learning for the life sciences

Key words: machine learning
Open to supervise

Computational palaeobiology

Using computational methods to understand life's evolutionary history

Key words: Palaeobiology
Open to supervise

Quantifying evolutionary convergence

Using computational methods to understand why evolution repeats itself

Key words: machine learning
Open to supervise

Ediacaran palaeobiology and palaeoecology

Understanding the early animals of the Ediacaran geological period (from 635 to 541 million years ago).

Key words: Palaeobiology
Open to supervise

Current research

Machine learning on butterfly phenotypes

Developing and applying spatial embedding methods for taxonomic classification, phenomic phylogenetics and analysis of mimicry evolution

Machine learning on the fossil record

Using new methods of machine learning to understand extinction and diversification in the history of life

Homoplasy: measuring evolutionary convergence on phylogenetic trees

Investigating the limits on basic measures of homoplasy

Computational Ediacaran Palaeobiology

Using computational methods to learn about Earth's early macro-organisms

Conferences and presentations

Session Chair: Animal, Cell, and Non-Human Communication

Invited presentation, After Babel: The Quest for Universal Communication, Japan, 23/4/2024

Quantifying phenotypic evolution: connections to information, complexity and predictability

Invited presentation, The Evolution of Complexity, Bath, United Kingdom, 29/7/2022

Cross University Research Event: Applications of AI

Invited presentation, University of Essex CURE, Colchester, United Kingdom, 20/4/2022

Testing the balance of mass radiation and extinction

Invited presentation, Department of Earth Sciences Seminar Series, Uppsala University, Sweden, 16/3/2022

Diversification through the looking glass: the continuum of mass radiation and extinction

65th Palaeontological association annual Meeting, Manchester, United Kingdom, 19/12/2021

Does the Ediacaran biota match Darwin’s predictions? Phylogenetic, taxonomic and evolutionary implications of the Precambrian fossil record

The Ediacaran Taxonomy Meeting, Oxford, United Kingdom, 4/10/2021

Machine learnt spatial embeddings for new biological insights

Invited presentation, The use of Machine Learning models in Behaviour, Ecology & Evolution, Neuchatel, Switzerland, 10/3/2021

The evolutionary decay-clock: persistence versus decay of evolutionary biotas through the Phanerozoic

The Palaeontological Association Virtual Annual Meeting, Oxford, United Kingdom, 16/12/2020

(Marx, Musk and) the Ediacaran Biota

Invited presentation, Open University Geological Society Annual General Meeting, Open University Geological Society Annual General Meeting, Milton Keynes, United Kingdom, 8/2/2020

Putting the AI into Palaeontology: using new methods of machine learning to capture evolutionary history

Palaeontological Association Annual Meeting, The Palaeontological Association 63rd Annual Meeting, Valencia, Spain, 20/12/2019

Faculty-track research fellowships: an interdisciplinary example

Invited presentation, Getting a lectureship for physical science postdocs, Getting a lectureship for physical science postdocs, 9/12/2019

Ediacaran origins of complex animal behaviour: trace fossil evidence from the Hoogland Member of Namibia

International Meeting on the Ediacaran System and the Ediacaran-Cambrian Transition, Guadalupe, Spain, 20/10/2019

Teaching and supervision

Current teaching responsibilities

  • Genetics and Evolution (BS102)

  • Animal Evolution, Ecology and Behaviour (BS113)

  • Transferable Skills in Life Sciences (BS143)

  • Employability Skills for the Biosciences (BS211)

  • The Theory of Evolution (BS347)

  • Methods in Marine Biology (BS707)


Journal articles (19)

Hoyal Cuthill, J., (2022). Ediacaran survivors in the Cambrian: suspicions, denials and a smoking gun. Geological Magazine. 159 (7), 1210-1219

Hoyal Cuthill, JF. and Hunter, AW., (2020). Fullerene‐like structures of Cretaceous crinoids reveal topologically limited skeletal possibilities. Palaeontology. 63 (3), 513-524

Conway Morris, S., Smith, RDA., Hoyal Cuthill, J., Bonino, E. and Lerosey-Aubril, R., (2020). A possible Cambrian stem-group gnathiferan-chaetognath from the Weeks Formation (Miaolingian) of Utah. Journal of Paleontology. 94 (4), 624-636

Hoyal Cuthill, J., Guttenberg, N. and Budd, GE., (2020). Impacts of speciation and extinction measured by an evolutionary decay clock. Nature. 588 (7839), 636-641

Han, J., Conway Morris, S., Hoyal Cuthill, JF. and Shu, D., (2019). Sclerite-bearing annelids from the lower Cambrian of South China. Scientific Reports. 9 (1), 4955-

Hoyal Cuthill, JF., Guttenberg, N., Ledger, S., Crowther, R. and Huertas, B., (2019). Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model. Science Advances. 5 (8), eaaw4967-

Wood, R., Liu, AG., Bowyer, F., Wilby, PR., Dunn, FS., Kenchington, CG., Cuthill, JFH., Mitchell, EG. and Penny, A., (2019). Integrated records of environmental change and evolution challenge the Cambrian Explosion. Nature Ecology and Evolution. 3 (4), 528-538

Hoyal Cuthill, JF. and Han, J., (2018). Cambrian petalonamid Stromatoveris phylogenetically links Ediacaran biota to later animals. Palaeontology. 61 (6), 813-823

Shu, D., Conway Morris, S., Han, J., Hoyal Cuthill, JF., Zhang, Z., Cheng, M. and Huang, H., (2017). Multi-jawed chaetognaths from the Chengjiang Lagerstätte (Cambrian, Series 2, Stage 3) of Yunnan, China. Palaeontology. 60 (6), 763-772

Hoyal Cuthill, JF. and Conway Morris, S., (2017). Nutrient-dependent growth underpinned the Ediacaran transition to large body size. Nature Ecology and Evolution. 1 (8), 1201-1204

Hoyal Cuthill, JF., Sewell, KB., Cannon, LRG., Charleston, MA., Lawler, S., Littlewood, DTJ., Olson, PD. and Blair, D., (2016). Australian spiny mountain crayfish and their temnocephalan ectosymbionts: an ancient association on the edge of coextinction?. Proceedings of the Royal Society B: Biological Sciences. 283 (1831), 20160585-20160585

Hoyal Cuthill, JF., (2015). The morphological state space revisited: what do phylogenetic patterns in homoplasy tell us about the number of possible character states?. Interface Focus. 5 (6), 20150049-20150049

Hoyal Cuthill, JF. and Charleston, M., (2015). Wing patterning genes and coevolution of Müllerian mimicry inHeliconiusbutterflies: Support from phylogeography, cophylogeny, and divergence times. Evolution. 69 (12), 3082-3096

Hoyal Cuthill, J., (2015). The size of the character state space affects the occurrence and detection of homoplasy: Modelling the probability of incompatibility for unordered phylogenetic characters. Journal of Theoretical Biology. 366, 24-32

Conway Morris, S., Hoyal Cuthill, JF. and Gerber, S., (2015). Hunting Darwin's Snark: which maps shall we use?. Interface Focus. 5 (6), 20150078-20150078

Hoyal Cuthill, JF. and Conway Morris, S., (2014). Fractal branching organizations of Ediacaran rangeomorph fronds reveal a lost Proterozoic body plan. Proceedings of the National Academy of Sciences. 111 (36), 13122-13126


Hoyal Cuthill, J. and Charleston, M., (2012). Phylogenetic Codivergence Supports Coevolution of Mimetic Heliconius Butterflies. PLoS ONE. 7 (5), e36464-e36464

Cuthill, JFH., Braddy, SJ. and Donoghue, PCJ., (2010). A formula for maximum possible steps in multistate characters: isolating matrix parameter effects on measures of evolutionary convergence. Cladistics. 26 (1), 98-102

Grants and funding


Is evolution predictable? Unlocking fundamental biological insights using new machine learning methods

Medical Research Council

+44 (0) 1206 873307


3SW.5.39, Colchester Campus

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