Industry Placement and Internship Opportunities

We are excited to announce that the University of Adelaide and CSIRO have now launched an elite industry-focused research training PhD Program with the first students starting in early 2019.

Our 4 year Industry PhD Program includes a minimum six month industry placement offering you an enhanced opportunity to work in an industry-defined research area with support and supervision through a partnership between the University of Adelaide, CSIRO, and an Industry Partner.

In addition, you will have the opportunity, along with your supervisory team, to engage in training and professional development activities. By building your abilities, skills, understanding, and encouraging professional growth, the iPhD will prepare you to hit the ground running after completion.

Within this unique program, the University and CSIRO will:

• administer and support the program
• provide a $40k p.a. scholarship
• provide project research support funds of $10,000 per annum.

Please visit the Adelaide-CSIRO iPhd website for more information on this program and scholarship

Australian Postgraduate Research (APR) Intern is Australia’s only all sector—all discipline national postgraduate internship program.

APR.Intern facilitates short-term 3-5 month research HDR internships across all study disciplines and business sectors, including private sector, government, and not-for-profit organisations.  Internships are available in all states including major cities, as well as regional and rural areas. There are hundreds of research internships available each year.

APR.Intern offers an additional path for industry to encourage development and innovation through research projects, giving postgraduate students the opportunity to apply their research expertise to projects while gaining invaluable industry experience.

APR.Intern also supports industry-based training of PhD research students in all Australian universities, with the goal of increasing employability and broadening business and university engagement.

Available Internships

For available internships please visit the APR.Intern website. From time to time, the University of Adelaide may also highlight specific APR.Intern opportunities on this webpage.

The Benefits

Apply Your Research: Turn your PhD theory into practice on an industry problem

Build Your Career: Build your industry networks and enhance your experience

Up Your Employability: Develop additional workplace skills to complement research expertise

Get Paid: Earn a stipend for the duration of the internship

Eligibility

Eligibility requirements can differ, however all of the following criteria must be met by all participating students:

• Be a PhD student currently enrolled at an Australian university
• PhD candidature must be confirmed
• Must be an Australian citizen, permanent resident, or an international student with the appropriate student visa
• Applicants must have the written approval of their Principal Supervisor to apply

How to Apply

For Information and application requirements, please visit the APR.Intern website or email APR.Intern

For further assistance, please also contact Nathan Crabe, Industry Research Placement Coordinator

If you are a student looking for a placement opportunity with an industry partner, professional development activities designed to increase your industry knowledge and the ability to learn from an industry mentor, develop additional workplace skills, and build your industry networks, then consider the Industry Engaged PhD (IEP).

You will apply your research knowledge and skills in a non-academic environment, guided by a University supervisor and an industry mentor. An IEP placement provides an opportunity for you to work on a real industry problem, which is distinct from your PhD research topic.

In addition, you will undertake selected Career and Research Skills Training (CaRST) activities as directed by the CaRST Director or industry partner. You can begin an IEP placement at any time from the confirmation of candidature to a maximum of one-month post-thesis submission.

How do I find a placement?
Students are encouraged to utilise their research networks to self-source a full-time placement opportunity of between 1-3 months in duration.

IEP scholarships are available on a competitive basis to support approved placements and are funded through the University of Adelaide. Alternatively, students can also access unique third party opportunities, which have their own timelines and conditions.

Please visit the IEP website for more information and details on how to apply

The UAiPhD is an exceptional and innovative 4 year program, which includes completion of a 6 month industry placement.

As a PhD student, you will have the opportunity to work on an industry problem, while being supported by a University of Adelaide supervisor and an industry supervisor for the duration of the program. In addition, you will undertake selected professional development activities offered through the University’s  Career and Research Skills Training (CaRST) program or as directed by the industry partner.

During your placement, you will gain valuable understanding on how organisations innovate and solve real world problems, and how you can make an impact with your research, while gaining a significant employment advantage.

The University of Adelaide offers successful candidates a 4 year scholarship.

Please visit the UAiPhD website for more information and details on how to apply

Duration: 3-5 months

APR.Intern, is Australia’s only all-sector all-discipline PhD internship program, transforming Australian businesses through short-term 3-5 month university research collaborations.

Due to increased demand from APR.Intern’s industry partners, we would like any PhD student, currently enrolled at an Australian university, who studies in the field of Data Analytics, to submit an expression of interest via our website.

APR.Intern will review all CV’s submitted and put forward eligible and relevant candidates to industry partners, with the aim of starting an internship prior to the end of 2019. Our industry partners include corporate organisations from finance and telecommunication as well as Government agencies at a federal and State level. To view APR.Intern’s industry partners, please check our website.

If this sounds of interest to you, please submit an expression of interest via our website and include why you would be interested in completing an internship with APR.Intern.

To view our currently advertised internships, please also check our website under the ‘Available Internships’ tab.

Skills and Qualifications Required

We encourage all PhD students to apply should they meet one or more of the following technical criteria:

· Experience in statistical/data mining/data science/machine learning tools (e.g. SPSS, SAS)

· Knowledge of algorithms and data structure

· Knowledge of programming languages (e.g. Python, R)

· Experience and/or interest working with large data sets

· Ability to work in a multi-disciplinary team

· Strong written & verbal communication

 

Please visit the APR Internship website for more information and details on how to apply.

Satellite Mission Developer

Location: Adelaide, SA

Duration: 6 months

Proposed start date: As soon as possible 

Project Background

Myriota is a South Australian company who are global leaders in low-cost, low-power, secure direct-to-orbit satellite connectivity for the Internet of Things (IoT).

Myriota has pioneered a new way to retrieve date from anywhere on earth, either on land or at sea. Leveraging years of customer focused Research and Development and an extensive suite of patented innovations, Myriota delivers secure, long batter life connectivity. Everywhere. Always.

They are currently looking for an intern with a suitable background in Physics or Engineering to work with and develop satellite modelling tools to design Myriota’s next Satellite Constellation.

Research to be Conducted

· Constellation Modelling and simulation

· Developing satellite operations strategies

· Gathering Business Intelligence

Skills Required 

We are looking for a PhD student with the following:

· Background in Physics, Software and/or Engineering

· Knowledge of orbital mechanics

· Knowledge and interest of the small satellite and launch (New Space) industry

· Experience with Systems Tool Kit or similar modelling and simulation package suitable for analysing satellites and ground station networks

· General purpose programming experience (matlab, octave, python etc), including C#

· Experience in the solution of constrained and unconstrained optimization problems including the development of tools and best practice tools.

Please visit APR Internship website for more information and details on how to apply.

 

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Satellite Mission Support: Optimised Optical System Design

Location: Adelaide, SA

Duration: 5 months

Proposed start date: As soon as possible

Please note: Due to the sensitivity and security of this project, students must have Australian Citizenship to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

Inovor Technologies is a South Australian company offering specialist development services and satellite mission solutions. Inovor provide turnkey nanosatellite solutions for clients using our Apogee nanosatellite bus. The Apogee bus provides the power, pointing, mission control and telemetry systems all integrated into a lightweight modular structure.

Inovor have also developed a nanosatellite-based space situational awareness (SSA) mission to support the global space network is in development, as is a smart Earth imaging mission for direct-to-user services. The Hyperion mission will support the global SSA effort using a constellation of 12U nanosatellites in Low Earth Orbit (LEO), observing the Medium Earth Orbit (MEO) and GEO orbital bands. Hyperion will provide large amounts of disparate data into the SSN.

Inovor is developing several satellite mission applications and are looking for a PhD student that can help them support the imaging payload development, including optics design and imager selection.

Research to be Conducted

Inovor are interested in developing a mission optimised optical system design, including an optic and the imaging pipeline.

This imaging system will need to conform to small satellite volume constraints and accommodate all other critical satellite bus systems around the imager. The student would work under the supervision of an optics specialist to develop an optical design and an image collection pipeline, and be supported by electrical and mechanical team members to achieve an optimised design.

Skills Required

We are looking for a PhD student with the following:

· Optics design expertise

· Imager selection expertise

· Machine learning and computer vision expertise

Please visit APR Internship website for more information and details on how to apply.

 

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Satellite Mission Development: Signal Processing – Radio Frequency

Location: Adelaide CBD, SA

Duration: 5 months

Proposed start date: September 2019

Please note: Due to the sensitivity and security of this project, students must have Australian Citizenship to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

Inovor Technologies is a South Australian company offering specialist development services and satellite mission solutions. Inovor provide turnkey nanosatellite solutions for clients using our Apogee nanosatellite bus. The Apogee bus provides the power, pointing, mission control and telemetry systems all integrated into a lightweight modular structure.

Inovor are also developing a nanosatellite-based space situational awareness (SSA) mission called Hyperion, The Hyperion mission will support the global SSA effort using a constellation of 12U nanosatellites in Low Earth Orbit (LEO), observing the Medium Earth Orbit (MEO) and GEO orbital bands. Hyperion will provide large amounts of disparate data into the global space surveillance network. As part of this Inovor is looking for a PhD student to support a Radio Frequency (RF) beam steering application on the satellite.

Research to be Conducted

The project will explore approaches to RF beam steering to distinguish two RF sources in close proximity to each other. They will also investigate ways to characterise the signals in simple terms such as frequency and bandwidth. The student will develop a high-level system design for the receiving unit and develop the beam steering algorithms to achieve the desired beams/nulls. The student should also have experience working with prototyping hardware or software defined radios and be able to set up an experimental system to demonstrate the algorithm.

The student undertaking the project will be expended to develop algorithms that could possibly be deployed in dedicated hardware platforms, so knowledge of embedded systems would be useful but not necessary.

Skills Required

We are looking for a PhD student with the following:

Essential

· RF signal processing and beam steering
· Machine learning
· RF electronics

Please visit APR Internship website for more information and details on how to apply.

 

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Satellite Mission Development – Computer Vision

Location: Adelaide CBD, SA

Duration: 5 months

Proposed start date: September 2019

Please note: Due to the sensitivity and security of this project, students must have Australian Citizenship to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

Inovor technologies is a South Australian company offering specialist development services and satellite mission solutions. Inovor provide turnkey nanosatellite solutions for clients using our Apogee nanosatellite bus. The Apogee bus provides the power, pointing, mission control and telemetry systems all integrated into a lightweight modular structure.

Inovor have also developed a nanosatellite-based space situational awareness (SSA) mission to support the global space network is in development, as well as a smart Earth imaging mission for direct-to-user services. The company is looking for a PhD student to support the development of computer vision algorithms related to Earth Imaging.

Research to be Conducted

The project will explore processing/vision systems to detect objects/attributes and changes in a sequence of images. The student will work on test image sequences to demonstrate image processing techniques for object detection, pose estimation and change detection. These algorithms may then be implemented on flight equivalent hardware.

Skills Required

We are looking for a PhD student with the following skills:

Essential

· Computer Vision
· Computer Science
· Artificial Intelligence
· Machine learning and image processing

Desirable

· Matlab, C++, CUDA and/or Python

Please visit APR Internship website for more information and details on how to apply.

 

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Renew Yorke and Mid North Via Coworking Spaces

Location: Clare Valley, SA

Duration: 5 months

Proposed start date: September 2019

Project Background

Regional Development Australia (RDA) Yorke and Mid North is a partnership between the Australian, state and local governments to support the growth and development of Australia’s regions. RDA builds partnerships between governments, regional development organisations, local businesses, community groups and key regional stakeholders to provide strategic and targeted responses to economic, environmental and social issues affecting the regions of Australia. RDA Yorke and Mid North is keen to explore the option for creating one (or potentially multiple) coworking spaces across the region, in an effort to support emerging industries, develop entrepreneurial eco-systems and inject vibrancy into declining main street areas.

Providing a professional and connected place for founders and entrepreneurs to work on their business can significantly contribute to the success rates of start-ups and small businesses. The establishment of Lot Fourteen on the old RAH site in North Terrace, Adelaide, and the intention of the State Government to promote and support the development of entrepreneurs and start-ups, has flowed through into supportive policies and resources, including the office of the South Australian Chief Entrepreneur, the development of an ‘Innovation Framework’ to drive investment and create a pathway for economic and business attraction opportunities, and physical site re-development designed to create ‘hubs’ which will form ‘entrepreneurial neighbourhoods’, leading to the development of new ideas and new enterprises.

There is considerable hollowing out of regional Main Streets, which appears to be accelerating at both national and local retail level. RDA Yorke and Mid North has been engaged in some main     street renewal activities through community development delivery in Peterborough but developing these schemes sustainably is complex as is modelling how State and local government support individual businesses directly. RDA are keen to explore what this model could look at a regional level under Renew Yorke and Mid North in the same way that there is a Renew Adelaide model, and to determine the locations most suited to the application of a renewal program, and to determine the cost benefit of investment into these locations.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Experience and/or understanding of entrepreneurial mindsets and supports, small business development, and/or business development strategies

· Excellent analytical skills

· Experience in engaging with communities and business owners to discuss concepts, gather and evaluate feedback

· A thorough understanding of the challenges of economic development for regional Australia

Desirable

· A thorough understanding of South Australia’s economic development plan, particularly in relation to regional South Australia

· An understanding of Regional Development Australia’s role

Please visit APR Internship website for more information and details on how to apply.

 

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Project Tokyo – Blending Machine Learning and Human Expertise

Location: Adelaide, SA

Duration: 5 months

Proposed start date: ASAP 

Project Background

Data driven models for time series forecasting are proving to give reasonable predictions and forecasts. In combination with expert human judgement we expect that time sensitive and expert knowledge is critical in terms of influencing a final prediction.

Project Tokyo is an innovative blend of Machine Learning and Human Expertise which aims to bring together the best of both to enable unprecedented accuracy in sales and production forecasting.

The leap in capability is achieved by marrying machine learning experts’ knowledge and insights to bring predictions to a super-forecaster level of accuracy, timeliness, and trustworthiness.

Project Tokyo aims to take into consideration specific human configurable parameters that an operator can tune to provide a final prediction that is the result of combining a data driven model and an expert human judgement (biological neural network model).

Research to be Conducted

Research and design a consistent way of modelling human configurable human input parameters in combination with robust data-driven machine learning models for time series forecasting.

For example, a pharmaceutical company may have a historical sales dataset which can drive the data driven sales forecasting model. However, the data driven model does not take into account recent temporal events that would influence the outcome of sales, e.g. tweets from politicians or an outbreak of swine flu.

Objective 1 – Single tuneable risk slider.

Objective 2 – A configurable set of risk sliders each with configurable weightings to influence the model.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Python Programming

· Strong foundation in Mathematics and Statistics

· Machine Learning experience

Desirable

· Jupyter notebooks

· Angular

Please visit APR Internship website for more information and details on how to apply.

 

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Circular RNAs And Their Role in Amyotrophic Lateral Sclerosis

Location: Remote, based at the Candidate’s University

Duration: 3 months

Proposed start date: ASAP

Project Background

Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative disease that typically causes death due to respiratory failure within 3 – 5 years of diagnosis. To make matters worse, most ALS patients suffer from the sporadic form of the disease (sALS) for which no cause, genetic or otherwise can be deduced. Current research in the field indicates a wide variety of genetic, epigenetic and transcriptional changes all contribute to the development and progression of the disease. This suggests a personalised medicine approach is needed to identify actionable therapeutic candidates from individual patients.

Iggy Get Out are currently performing deep phenotyping on patients with sALS and have gathered whole genome sequencing, RNA expression, DNA methylation and protein expression data. However, they have not been able to assess the expression levels of circular RNA (circRNA) in these patients. Given that circular RNAs are abundant and evolutionally conserved, they would very much like to probe their association with, and relevance to sALS.

Research to be Conducted

Initially, you will be asked to perform differential expression analysis of circular RNA in a very small cohort of sALS patients and healthy controls. This can be done using CircMaker, CIRCPlus, circtools or another algorithm of your own choosing/design. If you are successful in this leg of the project, we would also like you to perform a similar analysis on a large cohort of sALS patients (approximately 540 patients) acquired from the New York Genome Project. Some tertiary analysis of any differentially expressed circRNAs should also be performed. Iggy Get Out would like to know if there is any complementarity with mature miRNAs, or other genes. Mapping differentially expressed circRNAs to the genomic regions from which they arose should also be performed. Iggy Get Out are happy to accommodate tertiary analysis strategies recommended/developed by the intern.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Knowledge and training in biostatistics

· Fluency in R and Python

Desirable

· Understanding of biological pathways

Please visit APR Internship website for more information and details on how to apply.

 

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Unbiased Interrogation of Genomic Data

Location: Remote, based at the Candidate’s University

Duration: 3 months

Proposed start date: ASAP

Project Background

Iggy Get Out are currently exploring the presentation of a highly multi-factorial degenerative disease in humans. There are thought to be a number of genetic, epigenetic and gene expression changes related to the progress of this disease. Many disease mechanisms remain poorly described and probably undiscovered. They want to try and take a new perspective on this disease and the genomic data derived from it. Their approach hinges on using machine learning to identify patterns in raw genomic data in an unsupervised, unbiased way.

Research to be Conducted

Iggy Get Out have recently acquired access to two large cohorts of patients and healthy controls. They want to try an entirely new approach to discovering interesting genomic features of these patients compared to healthy controls using standard next generation sequencing data in FASTQ format. This data is in the form of millions of short sequences of DNA that normally are aligned against a reference genome in order to discover differences that may be clinically relevant. They want to take these raw reads and use machine learning protocols such as neural networks to identify patterns that are associated with the disease of interest without introducing the bias of fitting the raw data to the reference genome. Once the machine learning algorithm has identified sequences of interest using read quality and read count data, these sequences can be mapped to genomic regions for further functional analysis and interpretation.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Experience in machine learning

· Fluency in R and Python

Desirable

· Understanding of biological pathways

Please visit APR Internship website for more information and details on how to apply.

 

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Developing a Model to Evaluate a Business Case to Achieve a Circular Economy in Recycling Waste Management at a Regional Level

Location: Adelaide CBD (with field work in Naracoorte), SA

Duration: 5 months

Proposed start date: September 2019

Project Background

Councils are generally price takers in waste management and are in a vulnerable position to changes in the waste market, as was experienced when the China National Sword Policy took effect in 2018. The Policy exposed gaps in the waste and resource recovery sector, which impacted all Councils across Australia. Recycling facilities had to lift their gate prices as recovered materials were either greatly devalued or markets no longer existed. In some cases, as experienced in the Limestone Coast, providers simply discontinued their services. Cost of service has nearly doubled, significantly impacting residents and council providing the service.

Smaller regional councils need to transport the recycling materials long distances to achieve effective recycling outcomes, with current systems requiring economies of scale. In addition, there are hidden streams such as plastic car bumpers and windscreens, that do not enter the recycling system via councils, but nevertheless, add a cost burden to businesses.

It is time for a different perspective and to closely investigate a more regionalised or localised circular economy. A council area could be viewed as an island, so recyclable materials could be recovered and reused within the area. Glass and plastics can be reused in road making, and paper and cardboard can be reused in many different ways.

Naracoorte Lucindale Council are seeking assistance to develop a business model; involving establishing a Material Recovery Facility (MRF) and assessing whether the costs in recovery, possible processing and reuse of recyclable materials within a regional context are achievable.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Understanding of waste/recycling management

· Background knowledge of approaches taken by other regions to address recycling

· Strong Quantitative and Qualitative skills

Please visit APR Internship website for more information and details on how to apply.

 

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Pedagogical Study of Options Education Tools: Statistical Methods And Analysis

Location: Remote, based at the Candidate’s University

Duration: 4 months

Proposed start date: September 2019 (Proposed 1 Month pause in October 2019)

Project Background

The RoToR Payoff Diagrams® was developed as an options learning tool for retail traders, providing a teaching path from the basics (essentially objective – theoretically based definitions / concepts) to practical trading (highly subjective / unstructured / laissez-faire methods). This model was developed to fill a perceived deficiency in the current, widely accepted method where fundamental concepts were taught in isolation from the underlying price chart, from which retail traders would ultimately trade from.

Enhance Your Options Pty Ltd has developed a proprietary product, the RoToR Payoff Diagrams® where the majority of concepts relating to options theory and trading can be displayed in a single view with a full range of customizable parameters for the simulation of scenarios and whatifs. This is orientated consistent with and overlaid onto the historical price chart, unique amongst the trading platforms presently available. The RoToR Payoff Diagrams® is a registered trademark and the subject of a patent application in several jurisdictions.

·  The project has already garnered the support of several associations involved with investing, trading options and technical analysis who are willing to promote the project to their members, resulting in a pool in the region of 40,000 prospective participants.

· Ethics approval from the relevant institution will be required.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Experimental design – to ensure that the present structure of the experiment is sound and can conceivably produce statistically valid results.

· Data manipulation, Statistical Analysis – to ascertain any statistically significant differences between the two groups, in relating to several criteria and concepts.

Desirable

· Interest in any or all the following; studies of pedagogical methods, options trading, behavioural finance concepts – Whilst this research project being offered is essentially an empirical task, an interest in the underlying concepts that the data pertains to would be a significant benefit.

Please visit APR Internship website for more information and details on how to apply.

 

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Multi-Criteria Decision Analysis For Combat Simulation Study

Location: Edinburgh, SA

Duration: 4-6 months

Proposed start date: ASAP

Please note: Due to funding requirements, students must have Australian or New Zealand Citizenship or Permanent Residency to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

DST Group use combat simulation to explore the potential impact of modifying technologies, tactics, concepts, and force structures in the context of military operations. Combat simulation requires modelling the physical characteristic of the terrain and one’s own and opposing forces, and behaviours that represent military tactics, techniques, and procedures for manoeuvre and engagement. The mission effectiveness of a combat system is then approximated from the stochastic simulation output. Ranking and selection is the analytical problem of determining the best military system from a finite set of options (with a specified probability of correct selection) by statistical analysis of a designed combat simulation experiment. Defence Decision-Makers (DMs) can use the outcomes from a ranking and selection analysis as evidence to support future defence capability acquisitions.

However, the Defence ranking and selection problem is characterised by a number of problematic aspects: operational effectiveness is often represented by multi-criteria metrics and not single measures; uncertainty and imprecision are common in combat simulation criteria measurement and DMs preferences; and standard ranking and selection procedures from simulations are normally limited to the independent and identically distributed (iid) assumption, which is not necessarily true for combat simulations.

Consequently, it is desirable to develop a Multi-Criteria Decision Analysis (MCDA) methodology to support upcoming decisions for operational capability of future defence capability options. An efficient and robust stochastic MCDA model with interdependency would enhance the analysis of combat simulations including ranking and selection of the best option based on multiple responses from combat simulation, thereby increasing their usefulness to a wider scope of problems. This would result in enhancements to DST Group’s capacity and capability to use combat simulation as a decision analysis tool.

DST Group is seeking researchers with expertise in computational mathematics, statistics or operations research. This problem will require the researcher to discover and apply a combination of conventional and new concepts to deliver a successful solution.

The researcher will be tasked with exploring generic examples of such solutions which can be consistently implemented across a range of simulations. The actual implementation and testing of such solutions in combat simulation will be executed in collaboration with technical simulation subject matter experts (SMEs).

Skills Required 

We are looking for a PhD student with the following:

Essential

· Expertise in Statistics, Computational Mathematics or Operation Research

· Familiarity with programming or scripting

Desirable

· Proficiency in at least one programming or scripting language

· Military domain knowledge or experience

Please visit APR Internship website for more information and details on how to apply.

 

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Bayesian Network Meta-Model for Casual and Decision Analysis of Combat Simulation

Location: Edinburgh, SA

Duration: 4-6 months

Proposed start date: September 2019

Please note: Due to funding requirements, students must have Australian or New Zealand Citizenship or Permanent Residency to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

DST Group use combat simulation as an analysis tool to explore the potential impact of modifying technologies, tactics, concepts, and force structures in the context of military operations. Combat simulation for analysis requires modelling physical characteristics of opposing forces and terrain, behaviours that represent military tactics, techniques, and procedures for manoeuvre and engagement against an active enemy. In a combat simulation model, inputs are often described as the factors affecting the combat effectiveness of the system, i.e., system settings and configurations as well as friendly and opposing forces and operating environments. Simulation outputs, on the other hand, are artificial observations of the system produced by the simulation model. In a simulation study, simulations are performed with alternative values of the inputs and the observed outputs are recorded.

The data are then used to draw inferences concerning the operating characteristics of the system. A combat simulation model, although simpler than real-world war-fighting, can still be complex. The repetition of simulations may be time consuming and the sheer size of simulation data sets can make them unwieldy. To avoid this inconvenience, simulation meta-models are used to represent the dependence between simulation inputs and outputs. The most commonly used meta-models are input-output mappings that project the values of simulation inputs to the expected values of outputs. They include: regression models, Kriging, Neural networks and Bayesian Networks (BN), etc.

In this research proposal, we propose to use BN as probabilistic multiple input and multiple output simulation meta-models. A BN meta-model is a representation for the joint probability distribution of random variables associated with simulation inputs and outputs and is used to calculate marginal and conditional probability distributions as well as expected values and other descriptive statistics related to the inputs and the outputs. That is, the complete probability distributions are modelled – not just expected values as is the case with the existing meta-models. The BN meta-model enables various what-if analyses that are used for studying the marginal probability distributions of the outputs, the input uncertainty, and the dependence between the inputs and the outputs. Additionally, it is used to examine the dependence between the outputs and perform inverse reasoning. As far as we know, such analyses are beyond the scope of the existing MIMO simulation meta-models. The BNs allow effective what-if analyses which could be time consuming if conducted based on raw simulation data. Overall, the BNs offer a flexible approach to metamodeling of combat simulation. An efficient and robust BN meta-model would enhance the analysis of combat simulations including what-if, inverse, causal and decision analysis, thereby increasing their usefulness to a wider scope of problems, as well as reduce the computational effort. This would result in enhancements to DST Group’s capacity and capability to use combat simulation as an analysis tool.

DST Group is seeking researchers with expertise in computational mathematics, statistics or computer science. This problem will require the researcher to discover and apply a combination of conventional and new concepts to deliver a successful solution.

The researcher will be tasked with exploring generic examples of such solutions which can be consistently implemented across a range of simulations. The actual implementation and testing of such solutions in combat simulation will be executed in collaboration with technical simulation SME’s.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Expertise in Computational Mathematics, Statistics, or Computer Science

· Familiarity with programming or scripting

Desirable

· Proficiency in at least one programming or scripting language

· Expertise in Machine Learning and Artificial Intelligence

· Military domain knowledge or experience

Please visit APR Internship website for more information and details on how to apply.

 

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Computer Assisted Decision Making in Combat Simulation

Location: Adelaide, SA (with remote options)

Duration: 4-6 months

Proposed start date: September 2019

Please note: Due to funding requirements, students must have Australian or New Zealand Citizenship or Permanent Residency to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.

Project Background

DST Group models combat encounters using combat simulation to explore the potential impact and interactions of new or modified technologies, tools or tactics. Further, simulations represent realistic uses of tactics and force mixes to ensure useful insights can be gained from the outputs. The outcome of the simulation can be sensitive to tactics, positioning and routes, in addition to the given technologies or force structures being directly tested by a given experiment.

Currently, many aspects of the development process rely on hand crafted solutions refined by experimentation. This can be a time consuming process. Further, solutions that work for an isolated engagement may not balance allocation of resources, or positioning across multiple engagements. Similarly, ideal tactics will change dependant on overall mission goals for the larger scenario.

An ideal solution would be able to take in mission parameters, scenario information and recommend possible tactics that can be further refined or connected to subject matter expert solutions.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Expertise in software engineering, computer science or related software development discipline

· Familiarity with machine learning techniques

Desirable

· Experience in geospatial information systems, computer vision, or related terrain analysis

· Experience in computer aided decision making

· Experience with machine learning libraries and frameworks

· Familiarity with Java and Python

· Familiarity with distributed computing and/or HPC, including GPU based techniques

Please visit APR Internship website for more information and details on how to apply.

 

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Electric Helicopter Rotor Hub Development

Location: Nationwide

Duration: 5 months

Proposed start date: ASAP

Project Background

Hyper Q Aerospace is developing a hybrid electric rotorcraft. The rotorcraft will use electric axial flux motors to rotate the rotorhead and attached rotor blades. This configuration will eliminate the need for gearboxes, transmissions and drive shafts as they exist in conventional helicopter design. Using an electric drive will offer a far more responsive RPM control of the rotorhead, and when combined with the solution of an allied project will allow Hyper Q Aerospace plans to build the first pure electric drive rotorcraft in the world.

The project is fundamental to the existence of the company. Hyper Q Aerospace intends producing a range of unmanned rotorcrafts based on the technology derived from this project. The primary outcome of this project will be to create a hybrid electric rotorcraft that will improve speed and carry greater payload than any comparable platform. By developing an electric rotorhead Hyper Q Aerospace can take advantage of alternative power supplies, as new electric storage systems come into being. This might include battery, fuel cell, electric umbilical or possibly and more efficient liquid fuel powered engines. As the Hyper Q Aerospace rotorcraft models are fully scalable they will significantly disrupt the entire market place, enabling cost effective implementation of any size model from a couple of metres of rotor disk diameter up to sizes well in excess of 15m.

Skills Required 

We are looking for a PhD student with the following:

Essential

· EQ – Communication; focus on a practical outcome; not bound by conventional thinking, respect of others, independent, responsible, a willingness to learn.

· IQ – CFD, helicopter aerodynamics, Matlab or Altair or similar, mechatronics, Solidworks or similar

Desirable

· electronic communication systems, sensor capture and analysis, optical and laser capture and analysis, hybrid electric systems

Please visit APR Internship website for more information and details on how to apply.

 

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Electric Helicopter Swashplate Development

Location: Nationwide

Duration: 5 months

Proposed start date: ASAP

Project Background

The rotorcraft will use small servomotors to replace a helicopter swashplate. This project will allow the functionality of a helicopter swashplate to be broken apart so that the properties of blade pitch angle control and rate of change of blade pitch angle become independent. When combined with the solution of an allied project, this project will allow Hyper Q Aerospace to build the first pure electric drive rotorcraft in the world.

The project is fundamental to the existence of the company. Hyper Q Aerospace intends to produce a range of unmanned rotorcraft based on the technology derived from this project. The primary outcome of this project will be to create hybrid electric rotorcraft that will improve speed and carry greater payload than any comparable platform. By developing an electric rotorhead Hyper Q Aerospace can take advantage of alternative power supplies as new electric storage systems come into being. This might include battery, fuel cell, electric umbilical or possibly, more efficient liquid fuel powered engines. As the Hyper Q Aerospace rotorcraft models are fully scalable we will significantly disrupt the entire market place enabling cost effective implementation of any size model from a couple of metres of rotor disk diameter up to sizes well in excess of 15m.

Skills Required 

We are looking for a PhD student with the following:

Essential

· EQ – Communication; focus on a practical outcome; not bound by conventional thinking, respect of others, independent, responsible, a willingness to learn.

· IQ – CFD, helicopter aerodynamics, Matlab or Altair or similar, mechatronics, Solidworks or similar

Desirable

· electronic communication systems, sensor capture and analysis, optical and laser capture and analysis, hybrid electric systems

Please visit APR Internship website for more information and details on how to apply.

 

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Passive Radar Algorithm Development

Location: Mawson Lakes, Adelaide, SA

Duration: 5 – 6 months

Proposed start date: September 2019

Project Background

Daronmont Technologies is a South Australian Defence Industry company that designs, integrates and supports complex electronics and software-intensive systems. In particular, Daronmont is collaborating with Australian Universities and with the Defence Science and Technology Group on the development of state of the art passive radar systems.

Passive radar technology utilises existing sources of electromagnetic radiation (e.g., digital television or radio broadcasts) in order to detect and track airborne and terrestrial targets. Due to the fact that the transmitter is not directly controlled by the passive radar system, and because the characteristics of the transmitted signal are very different from the classical radar sequences, complex and computationally intensive algorithms are required to identify and localise possible targets. Spatial and time diversity also play an important role in passive radar signal processing.

Research to be Conducted

The goal of this project is to advance the signal processing algorithms used in the current Daronmont passive radar system and enhance the radar’s capabilities. Based on the student’s skills and interests, the project may focus on different levels of signal and data abstraction, from physical (antennas) to adaptive filtering and various aspects of tracking and inference.

A significant part of the project will consist of practical feasibility studies, analysis and enhancement of various approaches to improving passive radar performance. Verification of these algorithms through modelling and simulation with synthetic and real world data will be an integral part of this activity.

Developing products for Defence presents a unique opportunity to integrate state of the art theoretical results into a rigorously tested system that is used in critical real world applications. The student will be exposed to all stages of product development, and will play an instrumental role in implementing and testing the student’s contributions in passive radar systems.

Skills Required 

We are looking for a PhD student with the following:

Essential

· Ability and desire to learn and master complex digital processing techniques

· Broad background in telecommunications, antennas or modelling of physical systems

· Familiarity with digital signal processing

Desirable

· Background in Electronics or Software Engineering

· High mathematical aptitude

· RF design

· High performance computing

Please visit APR Internship website for more information and details on how to apply.

 

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