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January 2026 (6pm CET). The deadline for referees to submit reference letters is 14 January 2026 (6pm CET). Please check our website https://www.molgen.mpg.de/IMPRS/application for more details. Tuition
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 days ago
connected to phylogenetic software, methods and models, molecular evolution, hybridisation and introgression, and the evolutionary histories of eucalypts, viruses, bacteria, and Archaea. The ANU Phylogenomics
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Evolutionary Bias Database for Nucleic Acid Structures (NA3D4U) Built with Help of AI Development and integration of microbial biomass database with AI supported collection and validation of data Genetic-Code
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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algorithms, together with innovative methodological approaches for the clustering/tiling/thinning/sparsening of complex apertures. Where to apply Website https://lavoraconnoi.unitn.it/incarichi-di-ricerca
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-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
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Postdoctoral Fellow in Evolutionary Systems Biology The Department of Zoology is one of the departments within the Faculty of Science and has approximately 80 employees including researchers, PhD
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to the development and implementation of approaches that interface with living systems through novel materials and algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance