PhD Scholarship in Multimodal Biomedical Imaging

PhD Scholarship in Multimodal Biomedical Imaging

Job No.: 663602

Location: Clayton campus

Employment Type: Full-time

Duration: 3.5-year fixed-term appointment

Remuneration: $35,013 pa tax-free (indexed plus allowances)

For scholarship procedures and conditions, please see: www. monash.edu/graduate-research/future-students/scholarships/scholarship- policy-and-procedures.

The Faculty will provide the tuition fee scholarship and Single Overseas Health Cover (OSHC) for the successful international awardee.

The Opportunity

A full PhD Scholarship is currently available for research into deep learning applications in multimodal medical imaging, a collaboration between Monash University and Siemens Healthineers. The scholarship will provide tuition fee (assessed according to university requirement) and a generous annual living allowance according to the Australian Research Council PhD stipend.

Monash University is a member of the Group of Eight, an alliance of leading Australian universities recognised for their excellence in teaching and research. According to US News 2022, Monash ranks among the Top 50 in the world and is also recognised as Reuter's Most Innovative University in Australia.

The Department of Data Science and AI (DSAI), Faculty of IT (FIT), is home to many world-class research programs in artificial intelligence. Monash Biomedical Imaging is one of the most advanced imaging centres in Australia, hosting many cutting-edge imaging equipment for biomedical imaging research. Siemens Healthineers is a leading global company that provides healthcare devices, solutions, and services. The Ph.D. candidate will have the opportunity to work with a team of world-leading researchers at Monash University and Siemens, access to state-of-the-art imaging equipment, and work with industry leaders.

The PhD candidate will be supervised by Associate Professor Zhaolin Chen at the Faculty of Information Technology, Monash University. They will perform research in Deep learning-based motion correction algorithms by analysing Positron Emission Tomography (PET) and / Magnetic Resonance Imaging (MRI) data to identify and correct motion-induced distortions, resulting in motion- free scans with improved clarity and diagnostic accuracy. Enabling the generation of high-quality MRI/PET scans free from motion-related distortions leads to significant improvements in artificial intelligence-based downstream workflows' (e.g., segmentation, classification, registration) efficiency and disease diagnosis.

Benefits to PhD students:

Obtain top-tier research training at Monash FIT within a renowned multidisciplinary team

Work with a cohort of talented students/fellows to address real-world problems and challenges in medical imaging

Access opportunities to collaborate with prominent industry partners like Siemens Healthineers and Monash Biomedical Imaging

Opportunity to have placement at Siemens Australia, depending on performance

Candidate Requirements

A successful applicant will have an excellent academic track record in AI, data science, computer science, computer vision, or a related domain (a background in medical imaging is advantageous)

Proficiency in Python programming or familiarity with deep learning frameworks like PyTorch, TensorFlow

Exceptional communication abilities, both written and verbal, in English

Applicants will be considered if they meet the criteria for PhD admission at Monash University.

Details of eligibility requirements, including English-language proficiency skills, to undertake a PhD in the Faculty of Information Technology are available at www. monash.edu/it/research/graduate-research/scholarships-and- support/scholarships-and-applications

Scholarship holders must be enrolled full time and on campus. Applicants who already hold a PhD will not be considered.

Successful applicants will be expected to enrol by Saturday 1 of June 2024, but there may be some flexibility regarding the date of commencement.

Enquiries and application materials

Associate Professor Zhaolin Chen ([email protected])

Applicants must contact Associate Professor Zhaolin Chen via email to discuss the project. Applications should include following documents (if applicable) and any other materials:

A cover letter that includes a brief statement of the applicant's suitability

A curriculum vitae, including a list of any published works, conference presentations and relevant work experience

A full statement of academic record, supported by scanned copies of transcripts and relevant certified documentation

An outline for a research plan that responds to the scholarship application

A copy of the applicant's honours or master thesis

English proficiency results meeting Monash enrolment requirement (if applicable)

Contact details of two academic referees

Shortlisted candidates will be interviewed (over Zoom if necessary). The interviews will be conducted in English.

References

research.monash.edu/en/persons/zhaolin-chen

www. drzchen.com

www. monash.edu/researchinfrastructure/mbi/linked-labs/biomedical-imaging- analytics-lab-linked-fit-mbi-lab

Closing Date

Tuesday 30 April 2024, 11:55 pm AEDT

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