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Field
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algorithms, and innovative mechanical design to replicate the remarkable flight capabilities observed in nature. The successful candidate will be part of a dynamic, multidisciplinary team of experts in
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grant, have worked to identify the sampling algorithm used by the brain, to show how the identified sampling algorithm can systematically generate classic probabilistic reasoning errors in individuals
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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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instructing or supervision of labs and tutorials. Prior teaching experience is preferred for candidates. It would be good if the candidate can also explore areas such as multimodal algorithms/techniques
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algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique
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or use existing simulation platforms to validate the developed algorithms and models. Analyse simulation data, and create visualizations to support research findings. Design and build prototypes
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences