135 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Norway
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- University of Oslo
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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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conferences and contributing to collaboration between NIBIO, NMBU, and national and international research partners. Professional qualifications (required) A Master’s degree in machine learning, artificial
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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representation through machine learning. The position is for a fixed term of 3 years and is part of the project “Reaching AI Projections Trustworthy for Unseen Rainfall Extremes (RAPTURE)”, funded by a European
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interpretable machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization
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that are deeply grounded in stochastic analysis and show also development of computational methods towards machine learning. The projects will focus on applications to risk-sensitive decision making and control
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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that are deeply grounded in stochastic analysis and show also development of computational methods towards machine learning. The projects will focus on applications to risk-sensitive decision making and control
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machine learning and advanced analytical approaches Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Show curiosity and a strong motivation for the subject
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services