Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
-
broad guidance of senior academics. They will operate with considerable autonomy—selecting appropriate methods, planning and prioritising tasks, and serving as a subject matter expert who provides
-
, and high-throughput construct activity screening pipelines. Support collaborative projects, engage with external users, and establish scalable, reproducible methods and datasets that can be applied
-
, and high-throughput construct activity screening pipelines. Support collaborative projects, engage with external users, and establish scalable, reproducible methods and datasets that can be applied
-
technologies in transient and stable plant systems. Collaborate on research-service projects across academia and industry, ensuring reproducible workflows, scalable methods, and high-quality datasets
-
technologies in transient and stable plant systems. Collaborate on research-service projects across academia and industry, ensuring reproducible workflows, scalable methods, and high-quality datasets
-
engineering methods relevant to immune function and therapeutic discovery. Desirable criteria Familiarity with disease-relevant immune models, such as autoimmune T cell activation, chronic inflammation
-
computer literacy and customer service skills. Sponsorship / work rights for Australia Work Rights: you must have unrestricted work rights in Australia for the duration of this employment to apply. Visa
-
asymptotic methods and their applications at the University of Sydney Base Salary, Academic level A $105,117 - $121,054 p.a + 17% superannuation About the opportunity The School of Mathematics and Statistics
-
. Curiosity and initiative – motivated to learn new methods, improve workflows, and contribute ideas to enhance the lab’s computational capabilities. Commitment to scientific excellence – values rigour