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, contributing to the goal of ensuring more sustainable food production. Responsibilities and qualifications A major challenge in computer vision lies in efficiently training scalable deep learning models
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science. Candidates should have documented skills in atomic-scale simulations of catalysis or surface science, and should have an excellent track record of developing Bayesian statistics-based machine
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modelling in physical systems and life sciences. Focus on advanced techniques and methodological advancements with real-world impact. Requires PhD in machine learning, experience in deep generative models
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aimed at combining new computational methods and machine learning in an efficient search for new sodium ion battery cathode materials with superior performance, earth abundant elements composition, and
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formation and ionic transfer through the SEI. Candidates interested in machine learning methods could also pursue the development of equivariant graph neural network for electronic scale simulation of battery
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machine learning, experience in deep generative models, and programming proficiency. Postdoc in Game Theory for Sustainable Short-Sea Shipping – DTU Management Kgs. Lyngby, Denmark Posted on 03/07/2024 Two
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develop new methods for e.g. clock and phase recovery that support real-time high-speed implementations. use machine learning to improve discrete modulation formats. develop MATLAB or python code
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damage on the environment, advanced skills in programming, machine learning and/or geospatial modelling are a clear advantage. A strong motivation for analytical research and excellent communication skills
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maintenance of some of the in-house platforms. Demonstrate proficiency in computer vision approaches. Experience in mapping, 3D reconstruction and segmentation are considered an advantage. As a formal
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modelling in physical systems and life sciences. Focus on advanced techniques and methodological advancements with real-world impact. Requires PhD in machine learning, experience in deep generative models