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to support your professional growth throughout the project. What we are looking for: You should be educated to PhD level in a relevant field such as Artificial Intelligence, Machine Learning, Computer Science
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an outstanding environment in which to develop machine learning tools and engage with an interdisciplinary community of researchers with an interest in AI for healthcare. The post holder will have opportunities
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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. This continuing Level B role offers the opportunity to combine high-quality teaching with research in biomedical signal processing, machine learning and related areas, while contributing to an inclusive, industry
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 22 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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,(e.g. bioinformatics, computational genomics) and have Machine learning, and bioinformatic genome analysis experience. computer science or bioinformatics, including bacterial population genomics and/or
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Can AI learn to reason like a human - and recognise when it isn’t sure? This PhD tackles that challenge in a high-impact setting: interpreting 3D digital models of rock formations built from drone
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required