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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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generated data sets of different sizes and measuring the environmental impact. This impact can be measured and calculated by our Software Energy Lab, which has multiple test machines with GPUs and AI
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weather prediction using Machine Learning approach (hybrid forecast). The app is also expected to be equipped with seasonal forecast for agricultural planning. You will co-design the short-, medium-, and
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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assess the integration of participatory monitoring approaches, remote sensing and local natural resource knowledge to improve decision making in the implementation of nature based solutions and inform
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Centrum Wiskunde & Informatica (CWI) has a vacancy in the Machine Learning research group for a talented 2 PHD-STUDENTS IN NEUROAI OF DEVELOPMENTAL VISION (M/F/X). Job description The Curriculum
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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spectroscopy data and AI, to automatically identify textile fabrics with high accuracy in real-world sorting conditions by (1) defining optimal spectral bands, spatial resolution, and acquisition speed; (2
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, creativity, rigor, ownership, and excitement to push research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, data management, and
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of reconfigurable nonlinear processing units (RNPUs, [Nature 577, 341-345, 2020[(https://www.nature.com/articles/s41586-019-1901-0). In this PhD project, you will work on the development of efficient machine learning