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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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addressing measurement quality issues related to respondent non-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory
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at https://cbu.w.uib.no/joshi-group/ . Co-supervisors include experts in machine learning and AI, Pekka Parviainen and Tom Michoel, alongside leading epidemiologists, Tone Bjørge and Kari Klungsøyr. The core
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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, or C++ Candidates without a master’s degree have until 31 August 2025 to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology
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, Machine Learning, Complex Systems Modelling, Space Physics, and Ultrasound, Microwaves, and Optics. The department provides education at the Bachelor, Master, and PhD levels. Contact For further information
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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
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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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. The HDL and SPKI research groups are part of the Centre of Research-based Innovation SFI Visual Intelligence that is a center-of excellence in machine learning research. The research groups are also active
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain