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Field
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have been received within the last three years, 1 year of experience with machine learning, natural language processing, AI tools and frameworks, data integration, and/or explainable AI. Proficiency in
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record of publications in top-tier venues such as SIGMOD, VLDB, ICML, NeurIPS, ICLR, or TPAMI. You may also: Have a strong background in machine learning, particularly foundation models for spatial data
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grade point average of 3.0 (based on a 4.0 scale) or its equivalent in the undergraduate curriculum is required Working knowledge of large language models, generative AI, prompt engineering, and AI ethics
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, we expect machine learning to be employed to improve accuracy and efficiency of numerical methods, combining advanced technology with scientific research. About the Department of Mathematics at UiB
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research teams; and Knowledge of Artificial Intelligence/Machine Learning (AI/ML). Instructions to Applicants: For full consideration, applicants must apply for Postdoctoral Research Fellow at https
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the potential to impact on protected groups and take appropriate action. Desirable Skills: Experience with machine learning or natural language processing. Knowledge of econometric methods for policy evaluation
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior