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) Cleaning and managing large datasets from administrative data sources or online learning platforms Causal machine learning (e.g., double/debiased machine learning (DML), causal forests, generic ML) Learning
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 22 days ago
conditions: Scientific area: Bachelor’s Degree in Systems Engineering. Work plan: Model-based digital twins supported by physics-informed machine learning for multiphysics modeling. 1. Grant duration and
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, in close collaboration with the machine learning group at the Department of Informatics, both at University of Oslo. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
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Assistant Professor - Information Systems, Operations Management, Supply Chain Management, and Busin
AI/ML methodologies , advanced analytics , and data-driven decision making to solve contemporary business challenges. Research methods of particular interest include machine learning , deep learning
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practical skills in the field of data science, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 01-Apr-26 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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the following is desired: - Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design - Generative and probabilistic modeling