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Responsibilities: Undertake research on algorithms and data systems for next-generation data preparation and data cleaning for data analytics. Produce research papers, reports, and presentations as required by
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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services research, implementation science or a related discipline e.g. medicine, nursing, allied health, genetic counselling, public health, social sciences. Experience in health services or implementation
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an emphasis on technology, data science and the humanities. We are seeking highly motivated individuals with a strong interest in cancer genetics and genomic medicine to join the research team under Associate
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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, Materials Science, or a related field with low-dimensional nanophotonics. Excellent publication records will be preferred. Experience in matlab, MEEP, RCWA and other softwares and algorithms. Excellent
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within/outside of research group, using appropriate presentations format Key Competencies and Requirements: PhD degree in bioinformatics or statistical genetics, computational biology, or related field
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or Mathematics or Statistics with a strong background in one or more of the following: AI, machine learning, Bayesian statistics, programming languages, logic and algorithms, formal methods, probability and
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studies using genetically modified mouse models, human islets, and multi-omics approaches. This position is ideal for researchers with a strong interest in lipid metabolism, autophagy/lipophagy, and