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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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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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-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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soil-plant interaction under different cropping systems and act in close collaboration with the bioeconomic modelling group. We are looking for a highly motivated and skilled researcher with a PhD degree
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for your next career stage. Your profile Recent PhD in Cell Biology, Neuroscience, Biochemistry or a related discipline. A strong academic track record, and at least one recent first-author publication
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diverse backgrounds to apply for our positions. YOUR PROFILE PhD in statistics, mathematics, biology, evolutionary anthropology or a related field Strong background in statistical and mathematical modelling