La Trobe University - IIT Kanpur Joint PhD Program Scholarship

Updated: about 3 hours ago
Location: Melbourne, VICTORIA
Deadline: 31 Oct 2023

La Trobe University is offering a graduate research scholarship for students to undertake a joint PhD with IIT Kanpur, India.

The IIT Kanpur-La Trobe University Research Academy is a globally recognised research partnership formed between the Indian Institute of Technology Kanpur and La Trobe University that brings the two organizations' research capabilities together to build a critical mass of resources and researchers who work with industry and Government to address the sustainability and liveability issues facing global communities. PhD students in our Joint Doctoral Degree Program will have opportunity to work across both countries on a solution-driven approach to a problem identified under ten multidisciplinary themes related to the Smart Cities development. Opportunities for collaboration with industry is considered a key to success in solving some of the research, development and deployment challenges under various theme areas. Find out more:

Applications for this scholarship are now open to Australian or New Zealand citizens or Australian permanent residents newly enrolling in a PhD. The application deadline is 31 October 2023.

Students undertaking the joint PhD program will be enrolled in a PhD at both institutions. Your supervisory team will comprise of academic staff from both institutions who will provide support and guidance throughout your research. As a student enrolled at both La Trobe and IIT Kanpur, you will have access to services and support provided by both institutions, including a range of professional and personal development programs.

You will begin your studies at La Trobe University where you will spend the majority of your time, but with an expectation that you will spend typically 12 months at IIT Kanpur. Travel to and study at the host institution will be subject to the usual immigration requirements.

The joint PhD includes a tailored program of progress monitoring to fulfil the requirements of both institutions. All candidates will write and submit a thesis for defence by oral examination. On successful completion of the program requirements, you will be awarded a PhD jointly by both institutions.

The successful applicant can commence at any time between 1 February and 1 July 204, at a La Trobe University campus, and willing to spend typically 12 months based at IIT Kanpur, India.

Available projects

There are a number of joint PhD scholarship available to applicants from the range of projects listed below, competitively awarded and selection is based on academic merit and suitability to the selected project. Please contact the lead supervisor for more information about these projects.

Project: Machine Vision Techniques for Robot Water Tank Inspection Systems (IITK-23002)

Lead supervisor: Associate Professor Robert Ross

Other supervisors: Dr Abhilash Patel (IITK)

Clean water is an essential ingredient in the prevention of disease and flourishing of society around the world. Both Australia and India face significant challenges around water scarcity. Australia is well known for droughts as the driest continent on earth. India faces problems around inadequate water pressure, pollution and supply across mega cities. Prior to being distributed out to residences and businesses, water is commonly stored in large (>100KL) water storage tanks. These tanks vary in construction including welded steel, bolted steel, concrete and plastic. These water tanks are key assets which need to be protected from contamination and degradation by water authorities. Regular tank inspection currently performed to assess both structural integrity and ingress points for contaminates or vermin which can cause dangerous degradation to the water supply of a region. Currently, most inspections are performed manually in a time-consuming process which can be slow, subjective, dangerous, difficult to measure degradation over time and difficult to assess all areas of the water tanks requiring inspection.

A PhD candidate will be involved in design of robotic sensor systems and machine vision to perform condition assessment of water storage tank assets in a manner which improves safety, performance and traceability for these critical pieces of infrastructure.

Project: The Adoption and Application of Technological Service Innovation in Aged Care Context: A Service-Dominant Logic Perspective (IITK-23003)

Lead supervisor: Dr Seyed Mohammad Khaksar

Other supervisors: Professor Murali Prasad Panta (IITK)

Projections indicate the aged care industry in both developed (e.g., Australia) and developing economies (e.g., India) will face a significant increase in the number of older adults requiring professional aged care (approx. 300%) by 2050. The emergence of smart technologies (e.g., wearables, social robots, IoT) in aged care service operations has enabled care-providing organisations to ensure real-time data streaming to improve the quality of life for older people with special needs. Smart technologies suggest innovative solutions for currently unresolved issues in service delivery within traditional business models and provide transparent, accurate, and reliable data for aged care services. These technologies intend to improve aged care service delivery, the cost-benefit of health care, healthcare monitoring, and social engagement among older adults. Despite all these potential benefits, the level of smart technology adoption in aged-care service ecosystems is still low. The continued use of these technologies may also raise concerns about ethical issues in human-technology interaction.

To better address the above issues, we are looking for a PhD candidate to work on the adoption and implementation of new technologies in the aged care sector. The application is open to Australian or NZ citizens, or Australian permanent residents.

The PhD candidate will conduct research in social scientific fields of technology and innovation, predominantly adopting qualitative (or mixed) research designs. The research project requires a critical perspective on the Australian and Indian aged care systems with a strong theoretical focus on the service dominant logic and organisational paradox theory. So, a candidate with either a marketing or management background may be eligible to apply for this PhD position.

Project: Machine Learning for Neoantigen Identification and Prediction in Cancer Immunotherapy (IITK-23004)

Lead supervisor: Professor Wei Xiang

Other supervisors: Dr Subhajit Roy (IITK)

Recently, the advancement of artificial intelligence/machine learning (AI/ML) technologies has fueled a new type of treatment called "personalized cancer therapy", representing the future of cancer immunotherapy - the fourth pillar of cancer treatment along with conventional chemotherapy, radiotherapy, and surgery. Immunotherapy aims to boost the body's immune system to fight cancer naturally. AI/ML algorithms can help doctors, medical engineers, and oncologists tailor a treatment plan or vaccine specific to each individual patient. Underpinning personalized cancer immunotherapy is neoantigen identification and prediction. Neoantigens are new and unfamiliar proteins produced by and unique to tumour cells. They allow the immune system to recognize and destroy a tumour. This project aims to develop novel AI/ML techniques for intelligent, automated, and trustworthy neoantigen identification and prediction. Specifically, the objectives of this project are as follows:

  • Develop new automated and accurate screening techniques to improve the identification of mutations in the DNA of tumour samples. This is instrumental in the success of neoantigen prediction.
  • Develop novel AI algorithms for cancer neoantigen prediction. We aim to analyse DNA mutations and predict which individual mutations are likely to produce neoantigens that can be recognised by the patient's immune system. Accurate prediction of neoantigens is vital to the development of personalized cancer vaccines and adoptive T-cell therapy.
  • Apply explainable AI (XAI) methods to improve the interpretability and trustworthiness of the techniques developed above.

  • Project: Artificial intelligence (AI)-based anomaly and vulnerability detection of Generative Adversarial Networks (GAN) attacks on smart grids (IITK-23001)

    Lead supervisor: Professor Damminda Alahakoon

    Other supervisors: Dr Shalinka Jayatilleke (LTU), Dr Ankush Sharma (IITK)

    Smart grid is an interconnected cyber–physical system with advanced technologies of fast communication and intelligence, which however is prone to numerous cybersecurity threats. Artificial Intelligence is becoming increasingly popular for detecting cyber assaults in smart grids. However, very few techniques have focused on countermeasures for assaults designed to trick AI-based models, pay little attention to fake-normal data traffic generated by Generative Adversarial Networks (GAN). This PhD research will address a major vulnerability in AI based smart grids by the design, development and trialling of defense mechanisms and approaches against the GAN attacks. This research will utilize infrastructure and expertise in smart grids, power systems and IOT in the Department of Electrical Engineering at IIT Kanpur and innovative unsupervised self-structuring AI and spatio-temporal data modelling techniques from the Research Centre for Data Analytics and Cognition, La Trobe University.

    Benefits of the scholarship

    • a stipend for up to three and a half (3.5) years, with a value of $33,500 per annum (2023 rate)
    • a Research Training Program - Fees Offset scholarship covering tuition fees for up to four (4) years.
    • a travel allowance to assist with travel between Melbourne and Uttar Pradesh, India and personal expenses while resident in the India
    • an allowance to relocate to Melbourne to commence the degree and publication/thesis allowance or RTP allowance
    • opportunities to work with outstanding researchers at La Trobe and IIT Kanpur, and have access to our suite of professional development programs

    In selecting successful applicants, we prioritise applications from candidates who:

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