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close relation with another PhD student in Université de Lille, France. The selected candidate will have the opportunity to learn form a consortium of 8 institutions (10 Beneficiaries, 3 Partner
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, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference for complex models, causal inference and
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, mathematics (Operations research) or Computer Science or Machine Learning) the master thesis must be included in the application Ideal Candidate: demonstrates experience or strong interest in modelling
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using machine learning or any other AI technique. Knowledge of CCS. Good oral and written presentation skills in Norwegian/Scandinavian language equivalent level B2. Personal characteristics To complete a
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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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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requirements Applicants must document academic qualifications in their field, equivalent to an Associate professor position. The successful applicant must be able to teach at all levels and to supervise Master
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on ROS2 (Robot Operating System) and best practice of use of Github. Knowledge and skills on methods in numerical optimization, machine learning, as well as knowledge on marine power and control systems
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deterministic PDEs and equations subject to stochastic perturbations, integrating approaches from machine learning algorithms, transport theory, and optimization. Examples of relevant equations include, but
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emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain snow-related parameters to constrain the dominant drivers of photosynthesis