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. https://doi.org/10.1136/bmj.m1295

[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. https://doi.org/10.25816/5ebcbd26fa7e4

[3] Koh D: Migrant workers and COVID-19. Occupational and Environmental Medicine 2020:oemed-2020-106626. https://doi.org/10.1136/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. https://doi.org/10.7717/peerj.10203

[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. https://doi.org/10.1186/1471-2334-13-188

[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). https://doi.org/10.3201/eid2607.200282

[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 [https://www.health.gov.au/resources/publications/cdna-national-guidance-for-remote-aboriginal-and-torres-strait-islander-communities-for-covid-19]

[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 [https://www.health.gov.au/resources/publications/australian-health-sector-emergency-response-plan-for-novel-coronavirus-covid-19]

[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. https://doi.org/10.1101/2020.10.07.20208819

[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 [https://www.health.gov.au/resources/publications/covid-19-testing-and-response-strategies-in-regional-and-remote-indigenous-communities-key-messages-for-health-services]

[11] Department of Health, Commonwealth of Australia. Impact of COVID-19 in remote and regional settings. 2020, Department of Health, Commonwealth of Australia [https://www.health.gov.au/resources/publications/impact-of-covid-19-in-remote-and-regional-settings]

[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 [https://www.health.gov.au/resources/publications/aboriginal-and-torres-strait-islander-advisory-group-on-covid-19-communiques]

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 myers@stats.ox.ac.uk.

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.

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 PDRA to develop statistical methods to study differences in healthy phenotypes and disease risks between human populations, and apply these to Biobank-scale datasets. This work aims to develop improved approaches to genetic prediction of disease risk by combining information among groups and/or modelling the evolution of traits through time.

The post holder will develop methods to understand whether strong differences in identified genetic predictors in different human populations, and lack of transferability of polygenic scores among human groups, reflect mainly genetic or environmental differences. They will also map the origins of disease-causing rare variants in time, and at fine geographic scales for example within regions of the UK, in order to understand the applicability of rare variant discoveries to other regions or populations. Leveraging genealogical approaches previously developed within the group represents a natural start-point for this project, but it will also be essential to integrate functional information and to leverage large-scale data on both phenotypes and variation, for datasets incorporating a range of ancestries. The UK Biobank and Genomics England data, in which we have ongoing work, provide two important examples.

This position is based jointly within 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, or 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 myers@stats.ox.ac.uk.

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.

Informing the COVID-19 response: mathematicians’ contributions to pandemic planning and response

(This is a guest post by Professor James McCaw, 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.)

COVID-19 has changed how we live our lives, and will continue to do so for some time yet. Australia has been fortunate in many ways. We have clearly defined borders which are able to be managed effectively. We have a highly functional public health system. Despite the challenges in Victoria in mid-2020, ultimately we increased testing and contact tracing capacity enough to suppress transmission. We have, for the most part, seen coherent leadership from our state and Commonwealth political leaders.

The ‘science’—from clinical and lab-based research to mathematical modelling—has been listened to and, again for the most part, acted upon.

But world class research is clearly not sufficient to manage the pandemic. The United Kingdom—the ‘home’, and I would argue ‘intellectual powerhouse’, of mathematical epidemiology—has suffered greatly. As has the United States and much of Europe.

In Australia, the field of mathematical epidemiology is still in development. It was only 2005 when Australia’s National Health and Medical Research Council made its first major investment, funding a ‘Capacity Building Grant’ in infectious diseases modelling to support public health. I was fortunate to be appointed as a post-doctoral research fellow under the scheme, as were a number of other now well-known mathematical epidemiologists including Professor Jodie McVernon (Doherty Institute) and A/Prof James Wood (UNSW), who trained in infectious disease modelling and mathematical physics respectively. The grant was led by Professor Raina MacIntyre, a prominent epidemiologist and media figure in Australia’s COVID-19 response.

From the outset, we were engaged with the Australian Government Department of Health’s Office of Health Protection, the body responsible for preparedness and response to public health emergencies in Australia. At the time, the focus was on SARS and pandemic influenza.

As a mathematician, I have maintained an open dialogue with the Commonwealth for over 15 years. Through contractual research, we have developed stochastic models for border incursions, examined optimal distribution strategies for limited supplies of antivirals, estimated the volume of Personal Protection Equipment (PPE) required in a response and examined optimal strategies for vaccination. These analyses informed the development and multiple revisions of the Australian Health Management Plan for Pandemic Influenza (AHMPPI). In February 2020, with input from me and colleagues, the AHMPPI was rapidly adapted for COVID-19.

Throughout this 15 year period, we regularly visited Canberra to sit around the table with Health leadership, including four different Chief Medical Officers of Australia and their advisors. Both parties learnt a lot through that collaboration. As a mathematician I learnt how to communicate the purpose, limitations and relevance of our models. The government learnt to appreciate what models could and could not do. What policy decisions they could and could not inform. We gained a shared understanding that deeply quantitative work primarily delivered qualitative insights. And we learnt to trust each other.

Trust—not just in the science, but in the people conveying that science—is, in my view, the most fundamental requirement for the effective contribution of scientific knowledge to policy and response.

As a qualified and trusted advisor, I have contributed in two ways. With my team, we have undertaken mathematical analyses and delivered those results to government. But my responsibilities also include interpretation and evaluation of the (global and emerging) literature. Can Imperial College’s COVID-19 modelling on ‘lockdowns’ be applied to Australia? Are optimal vaccination strategies developed for other countries applicable here? Are real-time analysis methods for a well-established outbreak—like those developed at the London School of Hygiene and Tropical Medicine—applicable to Australian case data?

I believe that Australia benefited from the deep engagement and trust developed between academics and the Commonwealth over 15 years. The trust lies not just with the advisors, such as me. The trust extends through to a cultivated broader trust in the scientific research performed by others and interpreted and evaluated by those advisors.

And with that, where does mathematical analysis make a difference?

Often, it is in what we (as mathematicians) may perceive as surprisingly simple points.

Epidemic theory describes how a pathogen spreads through the community. Scaling out the average duration of infectiousness, and ignoring some biological subtleties, the rate of change in prevalence I (the proportion of individuals who are Infectious in the population) is described by a non-linear ordinary differential equation:

dI/dt = (R0S − 1)I

where R0 is the ‘basic Reproduction number’, the number of secondary cases arising from a single case in an otherwise fully susceptible population, and S is the proportion of the population that is Susceptible.

With R0 > 1 and S sufficiently large (as it is at the beginning of an epidemic), prevalence (that is, I) grows exponentially and S decreases (as dS/dt = −R0SI).

Eventually, depletion of the susceptible pool (S) modified the dynamics. The resultant non-linear dynamics are what make infectious diseases both mathematically interesting, and conceptually challenging for public health policy makers to respond to.

In early 2020, my team delivered a report to government which explored the possible change in the total number of infections over the course of an epidemic due to various percentage reductions in transmissibility. For our purposes here, this is as simple as considering how the size of an epidemic depends upon R0, although we did not report the analysis in this way to government.

A textbook analysis yields the ‘final size equation’, which relates R0 to the size (as t→∞) of the epidemic, Z = 1 − S(t→∞):

Z = 1 − eR0 Z

This is a non-linear relationship. By February 2020, we suspected the R0 for COVID-19 was in the range 1.5–3. At the upper end of this range, a 50% reduction (due to say, some level of physical distancing) has an important but fundamentally challenging effect—an epidemic with a modified reproduction number of 3/2 = 1.5 still spreads explosively, resulting in a vast number of infections. But if COVID-19’s reproduction number was at the lower end, a 50% reduction would prevent the virus from spreading entirely as 1.5/2 = 0.75 < 1 and transmission cannot be sustained.

Such results are entirely unsurprising to us as mathematicians. We understand the importance of non-linearities and of features such as bifurcations. It is natural for us to view the transmission of an infectious disease as a dynamical system. But these important points are anything but intuitive and easily missed by decision makers.

With trust and open communication channels, important findings, as well as viewing the entire pandemic and our response to it as a dynamical system, proved influential in Australia’s early response.

Simple analyses emphasised the value of mathematical reasoning. They highlighted the risks of ‘intuitive linear thinking’ but they also demonstrated how mathematical analysis can overcome that limitation. Models can be used to anticipate or reason on the (positive or negative) impacts of alternative response strategies.

Subsequent scenario analyses (with more ‘realistic’ and nuanced models calibrated to COVID-19 epidemiology) laid the groundwork for our initial response and for the monitoring and evaluation of the ‘effective reproduction number’ of COVID-19 throughout 2020 and into 2021. Collectively, these mathematical capabilities have contributed to the Australian government’s risk-assessment process for managing the pandemic.

National policy guidance relies upon in-depth mathematical modelling and analyses, conducted both in-house, nationally and around the world. But while necessary, the existence of that research is not sufficient to have impact. To have impact, to be effective, also requires relationships, ‘translators’ and, above all, (mutual) trust.

Abel Prize: call for nominations

The Norwegian Academy of Science and Letters hereby calls for nominations of candidates for the Abel Prize 2021.

The Abel Prize recognizes outstanding scientific work in the field of mathematics, including mathematical aspects of computer science, mathematical physics, probability, numerical analysis and scientific computing, statistics and applications of mathematics in the sciences.

The Abel Prize amounts to NOK 7,5 million.

The Abel Prize may be awarded to one single person, or shared for closely related fundamental contributions. The first instalment of the Abel Prize was in 2003. For laureates up until 2021, please consult www.abelprize.no

The Norwegian Academy of Science and Letters awards the Abel Prize on the basis of a recommendation from the Academy’s Abel Committee, chaired by an Academy member and consisting of four further members elected amongst names put forward by the International Mathematical Union and the European Mathematical Society. The Abel Committee receives all nominations and may itself nominate candidates for the Abel Prize. The name of the Abel Laureate will be announced in March 2022. The award ceremony will take place in Oslo in May 2022.

We hereby invite you (or your society or institution) to nominate candidate(s) for the Abel Prize. Your nomination should be accompanied by a description of the work and impact of the nominee/nominees, together with names of distinguished specialists in the field of the nominee/nominees who can be contacted for an independent opinion. When nominating it is a requirement to take into account that the nominee has adhered to general guidelines for research ethics.

Your letter of nomination should be sent no later than September 15, 2021.

For further information and the nomination form, please consult http://www.abelprize.no/c53676/artikkel/vis.html?tid=53705

Australian mathematicians rise to the challenge of COVID-19

(This is a guest post by Dr Joel Miller, introducing a miniseries of articles/essays by Australian mathematicians involved in the pandemic response)

Mathematics plays an integral role in our daily lives.  A smart phone that guides you to your destination relies on mathematical routines to calculate your position, other algorithms find the optimal route, and yet others ensure that the communications from your phone are secure.  The central role that mathematics plays throughout scientific disciplines comes largely because our mathematical models of the natural world, built on observation, give us remarkable predictive power and allow us to design systems that perform optimally.

At the outset of the COVID-19 pandemic, we did not have time to do careful experiments comparing different policies before implementing them.  What we had was information about how efficiently the disease spreads, some hints that it could spread through asymptomatic or presymptomatic infected individuals, and estimates of the distribution of severity in different age groups.  That knowledge grew as different countries began to experience outbreaks.

Armed with this knowledge policy makers were forced to make decisions about their response.  They needed a way to turn this limited information about the mechanisms underlying disease spread into projections of what the future would hold.  Mathematical modelling was the tool that let us rigorously determine what consequences could follow from different policy decisions and different plausible disease properties.  The modelling effort relied on a wide range of techniques and modellers from different backgrounds and career levels, ranging from student to senior academic, as well as researchers working within health departments.

Lives have been upended by the COVID-19 pandemic, and by our response to it.  In this series some of the mathematical modellers who played a role in advising Australia’s (thus far) stunningly successful response give their perspective on the role that they played, showing how mathematicians at many levels played a key role in the decisions that led to COVID-19’s effective elimination in Australia.

SMRI International Visitor Program (NZ only)

The SMRI International Visitor Program is now open again, for visitors travelling from
New Zealand only. This program funds researchers in the mathematical sciences to travel
to Australia for research collaborations at Australian universities. If you have
existing or potential collaborators working in New Zealand, please encourage them to
make use of this opportunity.

Please direct any potential applicants to the SMRI website for the full terms and conditions,
and instructions on how to apply. Any further questions about the scheme can be
addressed to this email address.

At least initially, applications for this NZ-only IVP will be on a rolling basis as with
our Domestic Visitor Program. For Aus or NZ citizens or Aus permanent residents, the
application deadline is 2-6 months before the start of the proposed visit; for other NZ
residents, 4-6 months before the start of the proposed visit.

Terms and conditions for the IVP will be updated when travel to Australia from other
source countries becomes possible.

Anthony Henderson

Executive Director, SMRI

Workshop on the Intersections of Computation and Optimisations

MoCaO (Mathematics of Computation and Optimisation) is planning a new workshop for late 2021 which is sponsored by the ANU, UNSW and AMSI. This workshop intends to bring together researchers from the areas of computation, optimisation, computing sciences and engineering interested in the cross-fertilization of ideas around the following theme:

 Optimisation often faces unique issues when there is a need to efficiently compute. On the other hand, computational techniques at times utilise optimisation within their algorithms. Both areas fundamentally need to understand approximation in all its facets which is also fundamental to computation as are the associated notions of convergence. Indeed, recent research has blurred the boundaries between optimisation (continuous and discrete), computation and areas of computing science. The area of machine learning has crept into relevance everywhere. Recently research has turned to its use in computational techniques including the enhancement of optimisation algorithms and the cycle of cross fertilisation of ideas has continued to date.

Workshop Format

We intend to run the workshop in a blended format, involving a face to face component which will be held at the ANU mathematics school in conjunction with a simultaneous\parallel online format to which both group of participants will engage. Some keynotes will present in person (streamed online from ANU) and other will engage totally online in a remote format. We encourage local and international participants to take part in the online workshop. In addition to their keynote presentations, keynotes who will be invited to give a lectorial-discussion session that will promote research questions and engage emerging researchers in these areas.

Keynote Speakers

  • Prof Gerlind Plonka-Hoch (University of Goettingen, Germany)
  • Prof. Frances Kuo (UNSW)
  • Prof Stefan Wild (Argonne, USA)
  • Prof Stephen Wright (Wisconsin, USA) 
  • Prof. Ian Turner (QUT)
  • Prof. Claudia Sagastizabal (IMECC-Unicamp and CEMEAI, Brazil)
  • Prof Martin Berggren (Umeå University, Sweden)

Important dates:

Registration Opens: 07/06/2021
Workshop Dates: 22/11/2021 to 25/11/2021

Future Announcements and Grants:

We intend to follow up with regular announcements regarding workshop accommodation, details on format and software and funding opportunities for ECR, PHD and female participants. We also wish to draw female participants attention to the possibility of applying for the WIMSIG Cheryl E. Praeger Travel Award (support for attending conferences/visiting collaborators) and/or the WIMSIG Anne Penfold Street Awards (support for careering responsible while attending conferences/visiting collaborators).

Australian Institute of Tropical Health & Medicine course on disease modelling

The AITHM is running a short course on infectious disease modelling. Please circulate the below information and attached flyer to your networks, for any interested students and other parties.

What: Winter Short Course – Mathematical Modelling of Infectious Diseases
Date: 19th to 23rd July 2021
Location: Online and Townsville (great place to be in winter!)
Who for: Aimed at participants with a basic understanding of infectious disease modelling and some basic programming skills
Who by: The Australian Institute of Tropical Health and Medicine at James Cook University are running a short course
Duration: 5 days
Cost: $880, though there are up to 10 scholarships available.

For more information please see the attached flyer.

All applicants should contact the course organisers via email to express interest in attending either in person or online.

Applications submitted to: this email address
EOI deadline: 10th June 2021
Payment deadline: 30th June 2021