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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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-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
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organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
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volumes of audiovisual data is essential. The appointee must have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large
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research in a supportive and stimulating environment Access to state-of-the-art resources and excellent support for further learning and professional development The University of Helsinki offers
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environments. Willingness to continuous improvement based on constructive evaluation, self-reflection and learning. We offer to join a group tackling research questions in crop physiology, soil science, crop