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great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part
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regular basis with the experimental groups in the initiative. The position requires sound verbal and written communication skills in English. Swedish is not a requirement, but willingness to learn is
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clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
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-assembly mechanisms, identifying robust experimental signatures of collective properties, exploring practical applications, and utilizing artificial intelligence and machine learning to aid in this process
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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, computational materials science, computer science, or a related field, awarded no more than three years prior to the application deadline*. Background in physics-based battery modelling and/or machine learning is
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sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
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learning. The employment is full-time for two years starting from August 1st 2025 or by agreement. Apply latest April 7th 2025. Project description Geometric deep learning refers to the study of machine
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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial