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Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg | Sweden | 22 days ago
mechanisms in normal neural development (demonstrated by us and colleagues) and may harbor cues for novel treatment strategies. Omics data can be used in black box machine learning algorithms to classify or
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of the identified structures via stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and
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learning, bioinformatics or advanced statistical methods, to help explore molecular, imaging, clinical and/or epidemiological data. You will apply, adapt and develop machine learning approaches to provide
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innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be
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experience with Python Knowledge of energy systems analysis and modelling, AI and machine learning for data analysis. Experience with the modelling tool OSeMOSYS for energy and CLEWs application Awareness
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collected can be trusted for training machine learning (ML) models and run-time interference. Both the use of AI in products, as well as the collection of data, assume fast iterations that allow for rapid and
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advising on methods and systems, assessing quality and properties of data, assisting with resource allocation proposals, machine learning workflows, dataset curation, organization, and sharing, data
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across different occupations in the Swedish labor market. The tasks involve collaborating with our researchers to process occupational classifications, harmonize census data, develop machine learning
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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: Experience combining proteomics with genomic/transcriptomic data Specialized knowledge: Understanding of peptide-spectrum matching, FDR estimation, protein inference AI/ML proficiency: Experience with machine