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on hierarchical Bayesian models that allow us to integrate heterogeneous, but complementary, ecological and environmental data. Depending on the background and interest of the candidate, the work will focus on a
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are seeking a postdoctoral researcher to develop methods for analyzing large scale biodiversity and ecosystem function data. Our approach is based on hierarchical Bayesian models that allow us to integrate
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reaction mechanism generators describing urban air chemistry; Developing volatility estimates relevant for urban chemistry; Developing quantum chemistry-based approaches for organic new-particle formation
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scenarios for various urban environments; Updating and applying reaction mechanism generators describing urban air chemistry; Developing volatility estimates relevant for urban chemistry; Developing quantum
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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in software engineering, machine learning, artificial intelligence, and human-computer interaction, the Unit provides a strong foundation and supportive environment for doctoral studies. In
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from estimation and optimization theory to hardware design. The specific topic of the project falls in the intersection of statistical signal processing and applied mathematics and is in particular
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understanding of social networks and human-computer interaction are common to all our fields. Software engineering, signal processing, computer engineering and information security are solution-oriented
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subarctic species communities, population abundance estimation, and provide science-based advice for mitigating large carnivore-human conflicts. The person nominated for the position is expected to initiate
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no estimate of their correctness which severely hampers accurate estimation of the correctness of downstream analysis. In this project we will develop novel models for estimating the correctness of genome and