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Field
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verbal, written communication skills In-depth knowledge of deep learning, specifically VLM models, computer vision techniques (e.g., open vocabulary object detection, model distillation, VQA, test-time
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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-driven and machine-learning models. The role will also involve conducting materials synthesis and electrochemical studies, troubleshooting and improving automated workflows, analyzing research data, and
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Strong background in control, robotics and machine learning, and experience in areas like model predictive control, adaptive control, reinforcement learning, robot perception or manipulation. Programming
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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, Julia, C/C++ or similar is required. Experience with machine learning, analysis of climate or high-resolution model output, climate predictions/projections or environmental risk assessment is an advantage
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: To independently undertake research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and
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strong background in relevant areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant
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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