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Field
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completed a PhD in engineering or computer science, or an equivalent foreign doctoral degree recognised as comparable to a Norwegian PhD. Competence in developing and applying machine learning methods Good
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, Educational Technology, Learning Sciences, Human-Computer Interaction, or a related discipline. Strong interest in research relating to Generative AI, AI literacy, digital learning, workplace learning, or human
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areas of statistics, data science, and artificial intelligence (AI). The position will focus on developing and applying novel statistical, machine-learning, and AI methods to advance biomedical and
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optimisation; uncertainty and sensitivity analysis; and machine learning or AI-supported optimisation. Strong analytical and programming skills are essential. Relevant experience may include tools and languages
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, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno-economic study, design and analysis of integrated systems. Experience with energy system
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Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and
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combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the globe. Candidates with
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system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and written
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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are particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine learning