• Code MEDN8007
  • Unit Value 6 to 12 units
  • Offered by John Curtin School of Medical Research
  • ANU College ANU Joint Colleges of Science
  • Course subject Medical Science
  • Areas of interest Medical Science, Bioinformatics, Computer Science, Cell and Molecular Biology, Algorithms and Data
  • Work Integrated Learning Projects
  • Academic career PGRD
  • Course convener
    • Dr Eduardo Eyras
  • Mode of delivery In Person
  • Offered in Second Semester 2024
    See Future Offerings
  • STEM Course

The deluge of data from our newfound ability to sequence genomes and visualise molecules from thousands of cells and individuals will revolutionise healthcare. Computational solutions, from distributed computing to deep learning, are increasingly essential to biomedical progress. While the transformation at the interface of high-throughput experimentation and computer science is underway, biomedical research is generating data at a faster pace than we are able to process and interpret it. This presents an unheralded opportunity for motivated students to become the new leaders of the computational biology revolution.

This course is an opportunity for students with diverse disciplinary backgrounds — including but not limited to computer science, engineering, statistics, biology, physics and mathematics — to work on real-world biomedical problems using computational methods under the supervision of top researchers at the John Curtin School of Medical Research (JCSMR). This course will embed students into multidisciplinary teams to Identify and work on applications of innovative computational technologies to biomedicine. 

This course can be taken as either 6 or 12 units. Students who take the 12 unit version are expected to undertake more in-depth and sustained study.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Plan and pursue an independent investigation and evaluation of novel computational methods to address open questions in biomedical research.
  2. Systematically develop and apply relevant theory and methods and draw evidence-based conclusions to open biomedical problems using advanced computational technologies. 
  3. Explain advanced concepts in computational biology and develop a translational application. 
  4. Demonstrate accurate and efficient use and development of advanced computational methodologies to solve real-world biomedical problems, and their communication in writing.
  5. Demonstrate capacity for scientific reasoning and interpretation through the independent development and application of advanced methods in computational biology and their communication to experts and lay audiences. 

Work Integrated Learning


Students may engage with WIL partners (internal/external) as a component of the course.

Other Information

Admission to this course is subject to availability of a suitable supervisor and approval from the Course Convener.

Further details about potential supervisors and projects can be found via the JCSMR website: Talo Computational Biomedicine Courses | The John Curtin School of Medical Research (anu.edu.au)

Students must complete the Research Project Summary Form which can be obtained from the JCSMR HDR Student Administration Team by email

(jcsmr.hdr.sa@anu.edu.au) and submitted via the following link: https://students.science.anu.edu.au/program-admin/requesting-permission-enrol

There is a financial award available from the JCSMR Talo Computational Biology Talent Accelerator.

Selection for an award will be based on academic merit.

Indicative Assessment

  1. Project Proposal (10) [LO 1,4]
  2. Software Output (20) [LO 1,2,4]
  3. Final Report (50) [LO 1,2,3,4,5]
  4. Oral Presentation (20) [LO 3,5]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.


This course is available as 6 or 12 unit course. For each 6 units of enrolment, the expected minimum workload will consist of approximately 130 hours of total student learning time made up of independent student research, reading and writing. Students are expected to maintain regular contact with their supervisor and attend all supervisory meetings.

The expected workload if taken as a 6 unit course will consist of approximately 130 hours throughout the semester including approximately 65 hours of team meetings, event preparation and self-directed study, including developing skills in developing and/or using software tools, and 65 hours of computational design and data analysis. Students are expected to actively participate and contribute towards group scientific discussions, presentations in group meetings, and attend school seminars

Inherent Requirements

No specific inherent requirements have been identified for this course.

Requisite and Incompatibility

To enrol in this course you must have a bachelor degree or international equivalent in a science, technology, engineering or mathematics subject. Enrolment in this course is dependent upon the availability of a suitable supervisor and must be approved by the course convener. An expression of interest with a confirmed supervisor is required. This course has a limited number of places due to availability of supervision. Incompatible with MEDN3007.

You will need to contact the John Curtin School of Medical Research to request a permission code to enrol in this course.

Prescribed Texts


Preliminary Reading

An Introduction to Bioinformatics Algorithms. N.C. Jones & P. A. Pevzner. MIT Press. 2004 (https://books.google.com.cu/books?id=p_qzpkNVcUwC)

Mathematics for Machine Learning. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press (https://mml-book.github.io/)

Assumed Knowledge

Sufficient knowledge and experience in the development of software for data analysis will be required for this course. It is recommended to have successfully completed one or more courses on algorithms, programming, and data analysis.


Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 to 12 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
7.00 0.14583
8.00 0.16667
9.00 0.18750
10.00 0.20833
11.00 0.22917
12.00 0.25000
Domestic fee paying students
Year Fee
2024 $880 per unit
International fee paying students
Year Fee
2024 $1180 per unit
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
8664 22 Jul 2024 29 Jul 2024 31 Aug 2024 25 Oct 2024 In Person N/A
8665 22 Jul 2024 29 Jul 2024 31 Aug 2024 25 Oct 2024 In Person N/A

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