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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
A post doc position in theory of machine learning is available. The post doc is under the supervision of Professor Kasper Green Larsen, Aarhus University, Denmark. The focus of the research project
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statistical methods; will seek out and learn new methods to better solve problems > Experience with modern AI techniques and methods or desire to work on Applied Machine Learning Problems > Constantly questions
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Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal > Experience in setup of research
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug
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Social and Everyday explainability; application of Machine Learning (such as Reinforcement Learning), Symbolic AI techniques (such as formal systems), or NLP techniques, in Human-AI collaboration; Human
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functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model the molten salt
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Project 2: Experience working with large datasets and machine learning Reasonable proficiency in any coding language used in data science Mixed-methods research experience Online Application Required
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for improved cancer understanding, diagnostics, and