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or explainable AI or safety). Experience in machine learning, causal inference, image processing, human-robot interaction, or large language models. Experience in analyzing multimodal data (e.g., text, sensor
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university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
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The Department of Statistics employs about 15 researchers, teachers, doctoral students, and other staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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build the sustainable companies and societies of the future. The signal processing group carries out research in the areas of inference and wireless communications, with both acoustic and radio signals
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systems, or network analysis. Experience with methods for causal inference, or modelling of biological systems is also considered a merit, along with prior work involving large-scale sequencing data such as
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(causal inference and pathobiology). iii) Integrating knowledge of clinical implementation channels. Other tasks may also be assigned. Eligibility Students with basic eligibility for third-cycle studies
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) predict molecular expressions, (ii) translate computational insight into patient-behaviour, and (iii) implement both supervised and unsupervised methods to infer stroke risk from pre-operative input data
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and advanced causal inference methods to large-scale multi-omics datasets, national health registers, and other comprehensive health-related data sources. Duties The doctoral position is intended
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prehistoric individuals, to make inferences about demographic history, migration and admixture patterns, and signals of adaptation. The project will include computational analysis of archaic introgression and