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Field
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of a scientific mentor, you will gain experience and learn to conduct rigorous data analysis and cross-project data synthesis, identify critical technical and data gaps, and determine future applied
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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goals will also be explored during the later stages of the selection process. You should also have: solid knowledge of artificial intelligence and machine learning techniques, including their limitations
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and processes; - Experience with machine learning and AI tools. Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work
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platforms, including robotic systems, laboratory automation, and AI/machine learning-assisted approaches for experimental design, optimization, and materials discovery. The research will target advanced
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents
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of multi-modal datasets, including MRI/fMRI, behavioural and audio data Develop reproducible statistical and machine learning analysis pipelines Support grant writing, ethics submissions and project
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals