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. Ability to motivate research (importance/significance) 2. Potential for academic impact 3. Potential for policy impact 4. Innovation in method 5. Presentation ability/communication Each
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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Sciences, Political Sciences, Computer Sciences, or Engineering, with a strong track record (relative to opportunities) in research on disaster management, extreme contexts, and resilience including a body
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and develop an optimal (efficient, cost effective, easy to use) technological operational method for detecting and estimating wallaby populations and dynamics. What you’ll do: Develop a technological
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) GRADE 7, £39,355 - £45,413 pa Fixed-term Ref: 095593 A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials
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many-body methods for multi-valence-electron atoms, with a focus on transition metals of interest for spin-crossover metal-organic frameworks (MOFs). The applicant will be involved in the development
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gathering and/or recording of results using appropriate research methods. Participate in and/or present at conferences and/or workshops relevant to the project as required. Assist with the supervision
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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
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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
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optimisation models (e.g. using MiniZinc) and solving methods to demonstrate the transformative potential of optimisation technology in this application. Collaboration will be key, as you’ll join the DSAI’s