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of influential knowledge leadership bringing the School together with students, business and society in learning to make a difference. Over the last five years ULMS has engaged in extensive recruitment of academic
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observations, and remote sensing data to assess the impact of global change on ecosystem productivity and sustainability. You will develop novel algorithms to integrate data-driven machine learning and process
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well
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recruiting physics study programmes in Norway, both within applied and fundamental physics. It also provides a large set of courses within physics for other study programmes at NTNU. For more information about
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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, Python, and R. The candidate should have a strong capacity to understand processes underlying pro-environmental behaviour from different perspectives, enabling them to simultaneously understand, use, and
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, implement, and evaluate computational models that assimilate 2-photon data (60%) Use a computer programming language to create novel neural network simulations (models) that include realistic simulations
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate