The Cancer Plasticity and Dormancy Program is focused on discovering the fundamental control mechanisms that regulate cancer cell dormancy and cancer relapse. Our goal is to develop tools to predict relapse and develop new treatments to stop relapse before it occurs. There is a focus on understanding relapse in cancers that spread to the skeleton including, multiple myeloma, breast and prostate cancer.

The program has recently established the AllClear program, supported by a major collaborative research accelerator grant from the National Breast Cancer Foundation, to tackle cancer relapse in breast cancer. This is a major national and international collaborative program and flagship activity for the program. AllClear brings together interdisciplinary expertise in cancer biology, bone biology, immunology, clinical oncology, mathematics, computational biology and machine learning to dissect the cellular and molecular programs that drive cancer relapse. 

THE OPPORTUNITY

The Senior Computational Biologist or Bioinformatician is accountable for leading a defined program of computational and machine learning research within the AllClear initiative, under the strategic guidance of A/Prof Chaffer and Prof Croucher. The incumbent independently designs, leads and drives complex machine learning and computational biology projects, sets and manages progress towards program goals, and serves as the senior computational advisor for the Chaffer/Croucher laboratories and the wider AllClear consortium. They will drive the discovery of dormant cancer cells and the molecular programs that regulate dormancy through the development and application of state-of-the-art machine learning pipelines, coordinate collaborative approaches with international partners, supervise and mentor junior computational staff, and hold significant leadership roles in project execution, methodology development, and grant funding.

Senior Computational Biologists or Bioinformaticians are appointed on a salary scale depending on level of experience, performance and requirements of the role.

Salary: Up to $140,400 + 14% super + salary packaging (depending on experience)

Employment Type: 2 year term contract with possibility to extend

SNAPSHOT OF BENEFITS

• Generous salary packaging to save you income tax on your wages thereby boosting your monthly take home pay (max. $15,900 general expenses + $2,650 meals/accom)

• Ample opportunities for on-going training and development

• Stimulating, diverse and highly international research environment

• Flexible work arrangements e.g. start / finish times

• 18 weeks paid parental leave for both parents including paid superannuation

• A range of additional leave types to meet your personal needs including cultural leave, conference leave, community service and study leave

• Discounted Health Insurance

• Lifestyle discounts with our community partners

WHAT YOU WILL DO

Research Excellence

• Independence and Innovation: Independently determine the computational, mathematical and statistical methods to be used in AllClear research; contribute to the design and development of novel deep learning approaches suitable for large-scale, multi-omic analysis of dormant cancer cells, and apply established tools from the lab. This includes the development of foundation models for relapse prediction, single-cell trajectory inference across primary tumour, disseminated dormant and reactivated cell states, and integration of multi-omic (transcriptomic, epigenetic, proteomic), imaging and clinical data.

• Research Leadership: Lead a computational biology body of work based on machine learning approaches to advance the discovery of dormant cancer cells and the molecular programs that regulate dormancy, resulting in authorship on methodological and biological publications in leading journals and conference proceedings.

• Strategic Planning: Take a strategic view of the AllClear computational body of work, prioritising and planning research direction.

• National/International Recognition: Establish a profile in computational biology and machine learning for cancer research, evidenced by invited seminar presentations at national and international meetings and by recognised contributions to the scientific community.

• Collaboration: Serve as a first-line contact between the laboratory, AllClear program investigators, project managers and international collaborators relating to computational work. Coordinate methodology development and large-scale analyses with internal and external partners – including A/Prof Smita Krishnaswamy’s lab at Yale (for methods such as Cflows, AAnet and neural-ODE based approaches), and adapt, extend and apply these approaches to AllClear datasets. 

Team Leadership

• Supervision: Act as a principal supervisor or co-supervisor for computational research students, junior computational scientists and other members of the team; delegate tasks and provide technical guidance on complex problems. 

• Mentoring and Coaching: Advise and coach lab members to develop their scientific, technical, statistical and research management skills, and to translate high-level scientific goals into concrete computational approaches.

• Professional Growth: Manage own continuing development, including tracking developments in deep learning, single-cell analysis and manifold learning methods, and participate in Garvan’s employee development and mentoring programs.

Resources & Planning

• Funding Generation: Act as a named Investigator or Co-Investigator on external research grant submissions, contribute substantially to the computational sections of team grants and fellowships, and work towards securing independent or co-investigator funding to support the computational component of the program.

• Computational Infrastructure & Pipelines: Take responsibility for the efficient day-to-day operation of the AllClear computational infrastructure, including managing data-production and analysis pipelines, ensuring reproducibility, and establishing best practices for computational analysis.

• Data Governance and Ethics: Contribute to data-access governance for AllClear data; ensure all computational research conforms to good scientific practice, data-security requirements, and open-science standards where appropriate.

Institute Contributions

• Organisational Leadership: Participate in Garvan committees, training programs, and fundraising events to promote a culture of collegiality, and contribute to Garvan’s data science and machine learning community.

• Professional Service: Actively participate in relevant scientific societies (computational biology, machine learning, cancer research) and act as a peer reviewer for grants, manuscripts and machine learning conferences.

• Public Engagement: Engage in the communication of science, including translating machine learning approaches for lay audiences, to the public via media interviews, public seminars and patient advocacy engagement.

ABOUT YOU

The key skills and experience include:

• Education: A PhD in computational biology, bioinformatics, machine learning, applied mathematics, statistics, computer science, or a closely related quantitative discipline.

• Experience: 5+ years of relevant postdoctoral or equivalent experience. A demonstrated track record of designing, leading and delivering complex computational projects independently is essential.

• Project Management: Proven ability to manage multiple simultaneous long-term computational projects, translate high-level scientific goals into concrete computational approaches, and coordinate collaborative analyses across institutions.

• Communication: Highly developed written and verbal communication skills for methodological and biological publications, grant writing, and presentations to both computational and biomedical audiences.

• Technical Expertise: Advanced expertise in machine learning and artificial intelligence, including deep learning frameworks, scientific programming (Python, R), high-performance and cloud computing, and single-cell analysis toolkits, and their application to single-cell multi-omic datasets. Familiarity with foundation-models for biomedical data and integration of imaging, molecular and clinical modalities is highly desirable.

• Interpersonal Skills: Excellent ability to build and maintain internal and external working relationships with a wide range of stakeholders, including experimental biologists, clinicians, computational collaborators and industry partners.

HOW TO APPLY

To apply for this position, please submit your application with a CV and cover letter as one document, stating why you are interested in this role. We are reviewing applications as they are received. If you think you’re the right person for this role, we’d love to hear how your capabilities, achievements and experience set you apart. Only applicants with full working rights in Australia are eligible to apply for this role.