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Field
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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Computer Science, Statistics, Finance, Economics, or a related field. Very strong programming skills, especially in Python. Hands-on experience with large language models, AI agents, or machine-learning systems
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preferential viewing behavior, using large-scale electrophysiology, behavioral experiments, and computational modeling. We welcome applications from recent PhD graduates who are interested in these or related
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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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facilities and heritage railways. Responsibilities will include coordinating demonstration logistics, collecting and analysing experimental and operational data, evaluating machine performance, and preparing
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transform visual information, and how neuromodulatory state changes, including exposure to drugs of abuse, alter visual processing and visual behavior. We combine large-scale electrophysiology with controlled
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, collecting and analysing experimental and operational data, evaluating machine performance, and preparing high-quality technical reports for industrial partners. The Research Fellow will also be expected
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proficiency in machine learning, statistical modeling, and data analysis using Python, R, or similar platforms. Experience in grant proposal writing, scholarly manuscript preparation, and psychological
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data, log-trace data from learning platforms, and panel data. Relevant areas of expertise include longitudinal data analysis, psychometrics, learning analytics, and machine learning. We are particularly
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with comprehensive baselines and validate results Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical