Casual Research Assistant – Artificial Intelligence for Software Engineering

Apply now Job no:545553
Work type:Casual
Location:Flexible
Categories:Information Technology, Research

Casual Hourly Rate: $43.47 per hour + 11% Superannuation

Employment Type: Casual (6 months, 20 Hours/Week)

Location: The applicant needs to be physically in Australia, but the location is flexible.

Company: Deakin University

About Us: The software engineering team at Deakin University is a dynamic and innovative research team committed to advancing the field of software engineering through cutting-edge research and development. We are seeking a talented and motivated Research Assistant to join our team and contribute to ground-breaking projects at the intersection of machine learning and software engineering.

Position Overview: We are looking for a Research Assistant with a strong background in machine learning and deep learning to work on exciting projects aimed at solving complex software engineering challenges. In this role, you will collaborate with our team of experienced researchers to extract and analyse data from software repositories, develop and apply machine learning and deep learning methods to address real-world problems in the software engineering domain. The role entails performing analysis and labelling of large data, critically reviewing research literature, and writing research reports.

Key Responsibilities:

• Conduct literature reviews and stay up-to-date with the latest developments.
• Collaborate with senior researchers to design and implement machine learning models and algorithms tailored to software engineering problems.
• Collect and pre-process data from various sources, including software repositories and logs.
• Develop and maintain software tools and pipelines for data processing and model training.
• Evaluate and experiment with different machine learning and deep learning techniques to optimise model performance.
• Assist in the development of research papers, reports, and presentations.
• Collaborate with interdisciplinary teams to bridge the gap between machine learning research and practical software engineering applications.

Qualifications:

• Master's, honours or PhD degree in Computer Science, Software Engineering, Data Science, or a related field.
• Strong background in machine learning and deep learning, with hands-on experience in building and training models.
• Proficiency in programming languages such as Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with software engineering concepts and practices.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
• Ability to work independently and as part of a team.
• This role requires the incumbent to apply for and maintain a Working With Children Check.

Preferred Qualifications:

• Previous research experience in machine learning, deep learning, or software engineering.
• Knowledge of version control systems (e.g., Git) and software development tools and repositories.
• Publications or contributions to open-source projects related to machine learning or software engineering.

What We Offer:

• Opportunity to work on cutting-edge research projects.
• Flexible working hours, and remote work.

How to Apply: Interested candidates should submit the following application materials. For a confidential discussion regarding this position, please contact Dr Hourieh Khalajzadeh, Senior Lecturer, Software Engineering - hkhalajzadeh@deakin.edu.au

• 2-page Resume/CV
• 1-page Cover letter detailing your relevant experience and interest in the position
• Academic transcripts (unofficial copies are acceptable)
• Contact information for at least two professional or academic references

Application Deadline: 22 Sep 2023

If you are passionate about research and eager to make a meaningful impact in the intersection Machine learning and Software Engineering, we want to hear from you!

Deakin University is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.