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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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advanced analytical approaches, including statistical modelling, machine learning, and/or genetic epidemiological methods, to study disease trajectories, risk factors, treatment patterns, and sources
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the application of machine learning), and optimisation of power systems for grid operators and energy producers The scope, depth and quality of the applicant’s scientific work The ability to independently conduct
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the opportunity for three weeks of training in higher education teaching and learning. The fellow will be expected to lead/contribute to research linking vegetation modelling with the LPJ-GUESS model with tree
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have a PhD in human–computer interaction, interaction design or a closely related field. You have documented experience of planning and conducting qualitative empirical studies of technology use
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school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, scientific computing, statistics, physics or a
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dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
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to the group’s open-source software, and participation in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, computer science
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of training in higher education teaching and learning. The purpose of the position is to develop independence as a researcher and to create the opportunity of further academic development. The postdoctoral
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patients with pancreatic cancer and other aggressive tumor types. The work includes statistical modelling, machine-learning and systems biology approaches to identify drivers of metastasis and treatment