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Field
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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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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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. Experimentation & Implementation - Develop and implement the related algorithms, models, and techniques. - Perform rigorous benchmarking and evaluation of the developed models. - Optimize algorithms for efficiency
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algorithms might support the wider integration of, and uptake of, renewable energy technologies for particular use cases and considering a variety of perspectives (technical/policy/social/economic). You will
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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Abrahao (NYU Shanghai) and João Sedoc (NYU Stern). Research Focus Areas Our research encompasses topics in DL and AI, including but not limited to: Deep Learning Algorithms and Paradigms Generative Models
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research and travel expenses. Relevant areas of expertise include: algorithms and complexity, natural language processing, and knowledge of the algorithmic fairness literature. The postdoc will be mentored
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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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tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and
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prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and