Indigenising University Mathematics 20-21 Sept: registration open – all welcome

Dear Colleagues,

You are warmly invited to register for “Indigenising University Mathematics” 20-21 Sept 2021, being held simultaneously online via Zoom and in-person at the Wollotuka Institute, University of Newcastle: 

This symposium is being put together to provide support, learning and collaborative opportunities around Indigenising our practices and teaching in University Mathematics and Statistics.  Increasingly, this is a responsibility that individual academics and University departments are feeling, but we do not necessarily know where to start. In some discipline areas, such as Food Science or Astronomy, the task may seem easier due to more obvious links between traditional Indigenous knowledge and course content.  In Mathematics and Statistics, the task may initially seem harder.  The purpose of this Symposium is to help.

It turns out that the challenges presented by Mathematics and Statistics may mean we may have an opportunity to do things which are deeper and more meaningful than simply incorporating isolated fragments of content, and we can do this in multiple ways.   We can utilise Indigenous pedagogies, for example using stories, symbols, maps and relationships.  We can promote inclusion and recognition.  We can compare with and learn from Indigenous ways of organising the world through structures such as kinship, that relate to graph theory and group theory and so on. And we can begin to (learn and) apply Indigenous perspectives to our own traditional content.  There is a lot to discover. 

In this symposium, we will utilise the traditional Indigenous practice of “yarning circles” to help us all get together and think through opportunities around all these and more.  To support this, the Symposium is organised around a number of themes, each of which is led by a small team of 2 or 3 Mathematicians/Statisticians/Indigenous practitioners. A presentation on each theme – see the Symposium webpage for more details – will precede the yarning sessions.  We hope to have broad representation from our Mathematics/Statistics and Indigenous communities, to facilitate sharing and the development of relationships and partnerships to support ongoing work in this area.

If you’d like to attend in person, please register soon, since places are limited to about 40 for in-person attendance, due to covid.  If you do register for in-person attendance and then cannot come in person, and you let us know by the week before, we will happily refund the difference and convert your registration to online. 

Feel free to contact me if you have any questions.

best wishes,

Judy-anne and all the organising committee.

Representation theory’s hidden motives: Conference at Münster and Sydney

The workshop takes place in-person at the University of Münster and at the University of Sydney, on 27 September – 1 October 2021. It can also be attended online. Workshop participation is free of charge. However, a registration is required. 

In recent years, motivic techniques have been applied in several branches of representation theory, for example in geometric and modular representation theory. The goal of this workshop is to bring together researchers in these areas in order to foster new synergies in topics such as foundational aspects of the theory of motives, Tate motives on varieties of representation-theoretic origin, motivic aspects of the Langlands program, and motives of classifying spaces.


Speakers marked (*) will speak in Münster, (**) will speak in Sydney.

Angeltveit, Vigleik (Canberra, **)
Cass, Robert (Harvard, *)
Coulembier, Kevin (Sydney, **)
Eberhardt, Jens (Bonn, *)
Fu, Lie (Lyon, *)
Haesemeyer, Christian (Melbourne, **)
Hoskins, Victoria (Nijmegen, *)
Kamgarpour, Masoud (UQ, **)
Lanini, Martina (Roma, *)
Levine, Marc (Essen, *)
Richarz, Timo (Darmstadt, *)
Semenov, Nikita (Munich, *)
Soergel, Wolfgang (Freiburg, *)
Spitzweck, Markus (Osnabrück, *)
Treumann, David (Boston College, *)
Vilonen, Kari (Melbourne, **)
Xue, Ting (Melbourne, **)
Yang, Yaping (Melbourne, **)
Zhao, Gufang (Melbourne, **)
Zhong, Changlong (Albany, *)


Nora Ganter (Melbourne)
Jakob Scholbach (Münster)
Matthias Wendt (Wuppertal)
Geordie Williamson (Sydney)

For more information, visit

Postdoctoral Research Fellow/Research Fellow

The School of Mathematics and Physics
University of Queensland

Closing Date: 3rd August 2021

The primary purpose of this position is to carry out high-quality mathematical research in the general area of Geometric and Nonlinear Analysis. Some contribution to undergraduate and/or postgraduate coursework teaching may be expected.

The position reports to the Head of School, Professor Joseph Grotowski.

This position is located at our picturesque St Lucia campus, renowned as one of Australia’s most attractive university campuses, and located just 7km from Brisbane’s city centre. Bounded by the Brisbane River on three sides, and with outstanding public transport connections, our 114-hectare site provides a perfect work environment – you can enjoy the best of both worlds: a vibrant campus with the tradition of an established university.

For more information and to apply, click here.

Associate Professor/Professor in Statistics or Data Science

School of Mathematical Sciences
The University of Adelaide

Closing Date: 12th August 2021

(Level D/E) $147,685 to $189,518 per annum plus an employer contribution of up to 17% superannuation may apply. 

Continuing position available from 1 January 2022.

The University of Adelaide is seeking a senior academic to lead the Discipline of Statistics in the School of Mathematical Sciences and contribute to the School’s strategic priority of expanding its research and educational offerings in data science, broadly construed.

This is an opportunity for an emerging or current academic leader to join a top-ranked team with ambitious plans for the future. The University of Adelaide received the highest possible rating of research quality in the mathematical sciences overall and in each of its disciplines in the two most recent ERA assessments. It was the only Australian university to receive top ratings for engagement and impact in the mathematical sciences in the 2018 Engagement and Impact Assessment.

The School is committed to pedagogical innovation and is currently working with its Industry Advisory Board to strengthen its external engagement as a strategic priority.

The School is strongly committed to increasing the diversity of its staff and students. We encourage and warmly welcome applications from academics who are able to contribute to the diversity of the School community. For more information and to apply, go to:

Mathematical modelling of Australian COVID-19 response: A PhD student perspective

(This is a guest post by Dennis Liu as part of our miniseries of articles/essays by Australian mathematicians involved in the pandemic response. A pdf version of this article is available here.)

It has been a little over 12 months since COVID-19 became a regular headline in the Australian media, but I would not be alone in saying it has definitely felt longer. At the time I was entering the third year of my PhD in mathematics and epidemiology, so when news broke of the new virus in late 2019, I was certainly paying attention. Little did I know it would affect not only my life as a researcher in the field, but everyone across the world.

Although COVID-19 restrictions have disrupted my study and research like many other HDR students, I have been fortunate that my work in modelling COVID-19 made progress towards my thesis.

In late February 2020 I was asked if I could help in the modelling effort, and at first this was supporting Dr Andrew Black and Dr James Walker in examining Australia’s pandemic preparedness and border closures. This modelling work by Andrew and James formed part of the advice on closing the international border. It was a frantic period of time, with a rapidly evolving situation.  Seeing this body of work influence policy was the first of many instances 

It wasn’t long after that my supervisor Prof. Joshua V. Ross asked if I was interested in developing and providing a forecast of COVID-19 cases to the COVID-19 response. I would be lying if I didn’t say Imposter Syndrome didn’t tell me to run the other way. Fortunately, and with encouragement from my supervisors and the wider COVID-19 modelling group, I didn’t give in and dived into the work.

To better describe our model, I will briefly introduce some important epidemiological concepts. An important epidemiological parameter is the effective reproduction number Reff, which can be defined as the average secondary number of infections from an infectious individual. This can vary through time, as behaviour changes through the epidemic, through social distancing and public health policy changes.

Reff can be retrospectively estimated through examining the number of cases over time, but to forecast cases using a mechanistic model, it must incorporate some estimate of the future transmission potential and/or arrival of infected cases. The relatively low number of cases in Australia also creates difficulties in utilising methods that rely on historical case incidence. Measures of mobility of each Australian jurisdiction provided by Google and survey results of the public’s behaviour in adhering to personal distancing measures provides the ability to link these indicators to an estimate of the effective reproduction number. This allows for a mechanistic model to forecast cases.

Figure 1: A schematic of the probabilistic COVID-19 forecasting model.

We estimated Reff using historical case incidence and an established method from the literature. To forecast Reff forward, we calibrated a model that links social mobility and personal distancing measures to these estimates of Reff

Within Australia, there have been jurisdictional level differences in policy and response to social distancing, but the underlying culture and mobility patterns may have commonalities. As such, we employed a hierarchical model to partially pool information between jurisdictions, while allowing for inferred differences where they may occur.

After calibrating the model and using Bayesian inference to learn the parameters, we then forecast the social mobility and distancing metrics using a random walk with drift in each jurisdiction. The model then gives a posterior predictive distribution on the Reff over time. 

The relatively few cases of local transmission in Australia, in conjunction with strict border control measures internationally and domestically, makes it natural to forecast the number of cases in each jurisdiction using a stochastic branching model. This generative model, using estimates from the literature for epidemiological parameters, can be paired with the time varying effective reproduction number to forecast COVID-19 cases in Australian jurisdictions. This framework adapts to changing public health policies and responses to the ongoing pandemic, particularly during small outbreaks and the irregular but frequent responses to outbreaks seen in Australia.

This forecasting model was run every week, and the results contributed to an ensemble forecast that was provided to various bodies in the Australian Government. This ensemble forecast was often considered by Chief Health Officers in determining the appropriate course of action, and was even shown a few times at media press conferences.

As mathematicians, it is rare that we get to personally observe the impacts of our research, let alone at my level as a PhD candidate. While the pressure and high stakes definitely gave me some sleepless nights, to see policy and action consider my work was incredibly fulfilling, and I highly recommend any HDR student take any opportunity to work on research with direct and immediate impacts like the COVID-19 response. Don’t let your Imposter Syndrome dissuade you from contributing, as every effort, however minor, helps. Your unique perspective will always be valuable in discussions, and you will almost certainly be supported by an amazing and dedicated team as well as your supervisors, as I did in my work!

Senior Mathematical Modeller

Burnet Institute

Closing Date: 18th July 2021

Burnet Institute have an opportunity for a Senior Research Officer / Senior Research Fellow to join our fantastic Modelling & Biostatistics team. This is a unique opportunity to develop and apply epidemiological and costing models around the world across a range of disease areas, including COVID-19, HIV, TB, malaria, viral hepatitis, maternal and child health.

This position is initially for a 2 year period.

Refer to the attached position description for full details.

Click here to apply.

Australian Mathematical Society expresses concerns about the proposed new mathematics curriculum

The Australian Curriculum, Assessment and Reporting Authority (ACARA) is currently developing a new national school curriculum, including for mathematics. The public consultation period is drawing to a close, finishing on the 8th July.

On the 2nd of July the President of the AustMS, Prof. Ole Warnaar, contacted David de Carvalho, CEO of ACARA, asking for an extension of the consultation period, and further details about the design process and evidence base for the proposed mathematics curriculum. This letter, along with Mr de Carvalho’s response, can be seen at this page.

The exchange of letters was followed up with a meeting on the 5th of July between Mr de Carvalho, Prof. Warnaar, and Prof. Geoff Prince, Vice-President of AustMS. One result is that “The meeting confirmed that mathematical scientists were not involved in any official capacity in the preparation of the revised curriculum.”

It is deeply concerning that the mathematics profession has been left out of the revision process and design of the new National Curriculum in Mathematics.

Prof Ole Warnaar

Prof. Warnaar’s full summary of the situation can also be seen at the letter page. At the time of posting there is to be no change to the consultation timeline.

Lecturer/Senior Lecturer in Statistics, Data Science, Stochastic Modelling

School of Mathematical Sciences
University of Adelaide

Closing date: 1st August 2021

(Level B, Lecturer) $100,933 to $119,391 or (Level C, Senior Lecturer) $123,075 to $141,537 per annum plus an employer contribution of up to 17% superannuation may apply. 

Three-year fixed term position available from December 2021.  On conclusion of the three-year term, the position may be converted to a continuing position under the provisions of the University’s Enterprise Agreement.  

Two full-time positions are available.

The University of Adelaide is seeking to grow the statistics, data science, and stochastic modelling team in the School of Mathematical Sciences. This is an opportunity for a highly motivated researcher and committed educator to join a School that is a leader in pedagogical innovation and received the highest possible rating of research quality in the mathematical sciences overall and in each of its disciplines in the two most recent ERA assessments.

The School has identified data science, broadly construed, as one of its strategic priorities. We are seeking an enthusiastic colleague to work with us to expand our research and educational offerings in statistics, data science, and stochastic modelling. Willingness to engage with industry would be an asset. 

The School is strongly committed to increasing the diversity of its staff and students. These two available positions are directed at applicants who are able to contribute to the diversity of the School community.

For more information and to apply, click here.

Contribution of mathematical modelling to COVID-19 response strategies in regional and remote Australian Aboriginal and Torres Strait Islander communities

(This is a guest post by Dr Rebecca Chisholm, Dr Ben Hui and Associate Professor David Regan as part of our miniseries of articles/essays by Australian mathematicians involved in the pandemic response. A pdf version of this article is available here.)

The health and science communities recognised early on in the SARS-CoV-2 pandemic that Aboriginal and Torres Strait Islander Australians were likely to be at high risk of COVID-19 infection and severe outcomes, due to high rates of comorbidities associated with severe outcomes [1,2], and multiple factors predisposing to increased SARS-CoV-2 transmission [2,3,4].  In March 2020, the Australian Government convened the Aboriginal and Torres Strait Islander Advisory Group on COVID-19 (IAG), co-chaired by the Department of Health and the National Aboriginal Community Controlled Health Organisation. The role of the IAG was to develop and deliver a National Management Plan to protect Aboriginal and Torres Strait Islander communities.  Our research groups—located at the Doherty Institute, the Kirby Institute and La Trobe University—were commissioned to carry out modelling, under the guidance of the IAG, to help inform aspects of this plan related to regional and remote communities.  

Our prior research and existing modelling frameworks enabled us to quickly begin the process of responding to the questions of interest to the IAG which included:

  • How important is a timely response to the first identified case of COVID-19? 
  • Who should be quarantined and/or tested in communities?
  • How important is it to test people when they are in quarantine and prior to exit from quarantine?
  • Is there a role for community-wide lockdown in initial containment? 

Together, we repurposed a stochastic, individual-based modelling framework which had previously been developed at the Kirby Institute to examine the dynamics of sexually transmitted infections in remote communities [5].  Within this framework, we incorporated a model of population mobility and household structure relevant to disease spread via close contact in remote communities. This model was originally developed at La Trobe University and the Doherty Institute as part of a research program focused on understanding the drivers of high prevalence of Group A Streptococcus disease in these communities [4].  We also integrated a COVID-19-specific disease transmission model and the effects of various public health responses.  Throughout this model building process, we regularly engaged with the IAG and representatives from other peak bodies and public health units to iteratively refine details and assumptions (described in Box 1 and Figure 1).  

To address the questions of interest to the IAG, we used the model to simulate and analyse a number of outbreak response scenarios. We designed the scenarios in consultation with public health service providers working closely with communities (with options varying by jurisdiction and community). These included:

  • Case isolation, with or without an exit test, and with various expected delays between case identification and response;
  • Case isolation and quarantining the contacts of a case (based on different definitions of contacts), with or without exit tests, and with or without tests on entry to quarantine;
  • Case isolation and population lockdown (entire community quarantined), with or without exit tests, and with various levels of assumed compliance to lockdown.

Box 1. Brief model summary. The individual-based, computational model we designed simulated the “silent” introduction of SARS-CoV-2 into a remote community of either 100, 500, 1000 or 3500 people, the subsequent transmission of SARS-CoV-2 within the community, and the public health response. The model explicitly represented the infection status of each community member, as well as their age and place of residence within the community, which were tracked and updated daily.  Community members were assumed to have close family connections across multiple dwellings in the community (their so-called “extended household”), between which their time at home was distributed, and within which they were at higher transmission risk compared to individuals staying in different dwellings (Figure 1a).  Infected community members were further classified according to whether or not they would present to healthcare services for testing (if symptoms developed and were recognized, and fear/stigma did not prevent individuals from presenting, Figure 1b). At the time we developed our model, there had been no  SARS-CoV-2 transmission in Australian Aboriginal and Torres Strait Islander communities.  Therefore, our model was parameterized based on the experience of SARS-CoV-2 in other populations [6], but accounting for the expected increase in transmission due to enhanced mixing anticipated in interconnected and overcrowded households [2,3].  

Two images: a) graphic showing population model with infectious and not infections people in various types of households in the community
b) flowchart with details on internal state of the disease model in the Infected phase
Figure 1. Schematic representation of the individual-based model. The model simulates the “silent” introduction of SARS-CoV-2 into a remote community, the subsequent transmission of SARS-CoV-2, and the public health response.  Here we illustrate the structure of the (a) population model; and (b) disease model.

To gain an understanding of the range of possible epidemic outcomes, we used our model to run 100 simulations of each outbreak response scenario  (defined by a set of parameters controlling the transmission of SARS-CoV-2, the public health response, and the assumed response of community members to the response).  For different response scenarios, we compared and reported the median and interquartile range of several model outputs of interest, including the percentage of the community who were infected at the peak of the outbreak (peak infection prevalence) and by the end of the outbreak (the attack rate), the number of cases identified versus the number of cumulative infections over time, the total number of person-days community members were in quarantine for, and the number of tests performed.  

We sought regular feedback on the response scenarios considered, and our interpretation and communication of model outputs.  This ensured we were always addressing relevant questions and faithfully relaying our findings (summarised in Box 2 and Figure 2). 

Our work informed both the CDNA National Guidance for remote Aboriginal and Torres Strait Islander Communities for COVID-19 [7] and the Australian Health Sector Emergency Response Plan for Novel Coronavirus (COVID-19) [8].  We have since submitted a publication for peer-review describing our work, currently available as a pre-print [9].  We also worked together with the IAG to develop a plain-language document containing key messages for health services [10], and a plain-language presentation [11] containing key messages for Health service decision makers and community leaders to consider when deciding how a remote community will respond to a COVID‐19 outbreak.   

To date, efforts to protect Australian Aboriginal and Torres Strait Islander peoples from COVID-19 are working – there have been no incursions of SARS-CoV-2 into remote Australian Aboriginal and Torres Strait Islander communities, and the incidence of locally-acquired cases among all Australian Aboriginal and Torres Strait Islander peoples is six-times lower than the Australia-wide incidence [12].  

Box 2. Brief summary of findings. Our analysis indicated that without an effective public health response, an introduction of SARS-CoV-2 into a regional or remote Australian Aboriginal and Torres Strait Islander community would likely result in rapid spread.  Furthermore, multiple secondary cases would likely be present in a community by the time the first case is identified, indicating that capacity for early case detection and a prompt response would be crucial in constraining an outbreak.  A response involving case isolation and quarantining of close contacts of cases defined by extended household membership was found to significantly reduce peak infection prevalence compared to the non-response scenario, but subsequent waves of infection consistently led to unacceptably high attack rates in excess of 80% in modelled scenarios.  Rapidly initiating an additional 14-day, community-wide lockdown of non-quarantined households could reduce the attack rate to less than 10%, but only if compliance with the lockdown was at least 80% (Figure 2).

Chart showing comparisons of epidemic curves based on different model assumptions
Figure 2. Impact of initiating a 14-day lockdown in addition to case isolation and quarantining of contacts with entry and exit testing on epidemic control. Epidemic curves for a community of 1000 individuals with various levels of individual compliance with community lockdown [9]


[1] Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H: Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. Bmj 2020, 368.

[2] Australian Institute of Health and Welfare: The health and welfare of Australia’s Aboriginal and Torres Strait Islander peoples: 2015. In. Canberra: AIHW; 2015.

[3] Koh D: Migrant workers and COVID-19. Occupational and Environmental Medicine 2020:oemed-2020-106626.

[4] Chisholm RH, Crammond B, Wu Y, Bowen A, Campbell PT, Tong SY, McVernon J, Geard N: A model of population dynamics with complex household structure and mobility: implications for transmission and control of communicable diseases. PeerJ 2020, 8:e10203.

[5] Hui BB, Gray RT, Wilson DP, Ward JS, Smith AMA, Philp DJ, Law MG, Hocking JS, Regan DG: Population movement can sustain STI prevalence in remote Australian indigenous communities. BMC Infectious Diseases 2013, 13:188.

[6] Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R: High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg Infect Dis 2020, 26(7).

[7] Communicable Disease Network Australia: National Guidance for remote Aboriginal and Torres Strait Islander Communities for COVID-19. 2020, Department of Health, Commonwealth of Australia []

[8] Department of Health, Commonwealth of Australia. Australian Health Sector Emergency Response Plan for Novel Coronavirus (COVID-19). 2020, Department of Health, Commonwealth of Australia []

[9] Hui BB, Brown D, Chisholm RH, Geard N, McVernon J, Regan DG: Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities. medRxiv 2020, 2020.10.07.20208819.

[10] Department of Health, Commonwealth of Australia. COVID-19 Testing and Response Strategies in Regional and Remote Indigenous Communities: Key Messages for Health Services. 2020, Department of Health, Commonwealth of Australia []

[11] Department of Health, Commonwealth of Australia. Impact of COVID-19 in remote and regional settings. 2020, Department of Health, Commonwealth of Australia []

[12] Aboriginal and Torres Strait Islander Advisory Group on COVID-19. Aboriginal and Torres Strait Islander Advisory Group on COVID-19 Communique Update: 14 December 2020. Department of Health, Commonwealth of Australia []

Postdoctoral Research Assistant 

Department of Statistics and the Wellcome Centre for Human Genetics
University of Oxford

Closing Date:30th July 2021

Grade 7: £32,817 – £40,322 p.a. 

We invite applications for a postdoctoral research assistant to develop predictive models for how DNA sequences impact regulatory networks, and apply these models to new single-cell datasets including for both gene expression, and chromatin accessibility (openness) during meiosis. The successful candidate will create new techniques integrating information from genomic DNA sequences, to perform mixture decomposition of sparse data matrices, and non-linear prediction, among other goals. We anticipate the resulting methods will be widely applicable to provide a technique for identifying the role of mutations, for example those identified in genome-wide association studies, in impacting gene expression in general. The postholder will work jointly at the Department of Statistics and the Wellcome Centre for Human Genetics. The post holder will join Oxford’s leading genomics research community, and the project may involve international collaboration and potential visits to collaborating groups.

The successful candidate will hold or be close to completion of a PhD/DPhil in a relevant quantitative scientific discipline, for example statistics, machine learning, mathematics, statistical or population genetics, or related disciplines. They also should have experience in developing and applying novel statistical methods, and low-level programming. Experience in analysing high-dimensional datasets, for example in computational statistics, machine learning, is highly desirable. They should have a strong interest in biological problems, genetics and/or genomics, but previous experience is not essential.

Queries about this post should be addressed to: Professor Simon Myers at

This post is fixed-term until 10 September 2023.

Only applications received before 12.00 midday on 30 July 2021 will be considered. Interviews will be held on 25 August 2021.

For more information and to apply, click here.