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Field
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-disciplinary areas of artificial intelligence machine learning big data and data analytics software and security mobility and autonomy The Presidential Postdoctoral Fellowship is proudly supported by generous
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MMA’s program and regional offices, exposing you to a variety of perspectives and issues. Learning Objectives: Under the guidance a mentor, you may gain skills and experience in the following areas: Gain
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with anxiety and co-occurring learning difficulties. Specific duties include recruiting and scheduling participants, making phone calls, assisting with research study visits, collection of cognitive
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Science, Materials Informatics, or a closely related discipline. 3–5 years of postdoctoral research experience with a strong publication record. Demonstrated expertise in machine learning for time-series or multimodal
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CW and pulsed laser systems, spectrometers, high-resolution cameras, and delicate optical components are desirable Expertise in advanced data analysis techniques (Machine learning and Deep learning
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that are only partially met by the development of special purpose classical computing units. This has motivated a recent interest in using quantum computing to machine learning tasks, in particular to clustering
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technologies like Azure Data Explorer (ADX), PostgreSQL, MongoDB, and data driven machine learning tools such as Spark, Power BI. Experience with optimisation, simulation and data analysis tools such as Matlab
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. Learning Objectives: By the end of this training/research experience, you will be able to: Explain the structure and functional organization of plant, insect, and/or fungal genomes and describe how genomic
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but not limited to the following: Develop new computational tools through the application of AI / deep learning / machine learning / statistics on spatial and single-cell omics (transcriptomics
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
foundation in computing and information sciences at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations