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. The PhD program at the Department of Biology includes many of these specializations, from molecular biology to applied ecology, from viruses and individual cells to evolutionary biology and global
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are not limited to models and algorithms for knowledge discovery, novel algorithmic and statistical techniques for big data management, optimization for machine learning, analysis of information and social
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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, resource efficient algorithms, and programming paradigms for enabling an application-tailored design of dependable communication and computation systems. Project description This PhD project is linked
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an integrated development of network architectures, resource efficient algorithms, and programming paradigms for enabling an application-tailored design of dependable communication and computation systems
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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general problem for all life that underwent endosymbiosis at some time during their evolutionary history. Depending on the applicant’s interests, the project can take several directions. One option is to
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their ability to: independently pursue his or her work collaborate with others, have a professional approach and analyze and work with complex issues. Experience in machine learning, algorithmic theory, or code
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of ecological processes involving animals and plants across a range of spatial and temporal scales understanding of raster data processing including the theory and implementation of relevant algorithms