10 parallel-computing-numerical-methods-"DTU" Fellowship research jobs at University of Adelaide
Sort by
Refine Your Search
-
equivalent qualifications or research experience. Outstanding verbal and written communication skills Demonstrated experience in research in evidence synthesis methodology and research methods and experience
-
computer vision and machine learning research group in Australia -- and contribute to world-leading research projects at the CommBank Centre for Foundational AI This postdoctoral research position is part of
-
metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
-
Public Policy, providing cutting-edge research in areas such as econometrics, macroeconomics, data analysis, and quantitative methods. Our ideal candidate is comfortable in working independently with a
-
in-house methods to develop null mutants in candidate genes via tissue culture and gene editing in Arabidopsis. The position will focus on pectin, and particularly on rhamnogalacturonan-II (RG-II
-
Capability is a $250 million enterprise powered by UoA and UNSW, with funding from the Australian Government through the Trailblazer Universities Program, as well as university and industry partners. Our
-
Wildlife Crime Research Hub as part of the ARC Industry Laureate Fellowship program, Combatting Wildlife Crime and Preventing Environmental Harm at one of Australia’s leading research institutions
-
to, and interpretation of, extreme material online. It will use in-depth qualitative methods to collect novel data and provide an evidence-base for points of intervention. It is part of an ONI Discovery
-
, with a focus on elucidating interfacial components and reaction mechanisms at the fundamental level. Potential to lead a research program in fundamental and/or applied areas addressing current battery
-
specific methodology and tackle experimental challenges creatively. To be successful you will need: Strong computing skills in R software and demonstrated experience in quantitative analyses, and model