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Field
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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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for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries of performance, speed, and precision
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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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over Morocco. Key Responsibilities: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in
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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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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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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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for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries of performance, speed, and precision
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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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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