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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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, including fitting and manipulation of large array-type data sets (using Python, Matlab or equivalent) Ability to communicate well, and work within a collaborative team environment Preferred Knowledge, Skills
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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. Position Responsibilities Develop and implement machine learning and deep learning models to analyze and interpret high-throughput functional genomics data, such as ChIP-seq, RNA-seq, and ATAC-seq
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are also developing novel machine learning methods to improve risk gene prediction and variant interpretation. This role will focus on the analysis of large-scale human genetics, scRNAseq, and proteomics