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related to staff position within a Research Infrastructure? No Offer Description ELLIS Institute Finland is a newly established world-class research hub in AI and machine learning – and we are growing! We
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machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
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and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
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related to staff position within a Research Infrastructure? No Offer Description ELLIS Institute Finland is a newly established world-class research hub in AI and machine learning – and we are growing! We
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more than five years ago at the time of accepting the position. In this context, the 5-year limit refers to a net period of time, which does not include maternity leaves, parental leaves, military service
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processing, machine learning, statistics or related fields. Demonstrated expertise in ML/AI, with prior experience of applications in the healthcare domain, particularly in cancer research considered a strong
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to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning