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Department of Evolutionary Anthropology (IEA) Professorship for Evolutionary Anthropology and Primatology 100% We are seeking candidates in the field of Comparative Evolutionary Anthropology and
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1-year fellowship in Shoebill Conservation Genetics with support for a further 3-year PhD fellowship
biology, or conservation completed before 31 July 2026. In addition to an interest in evolutionary and conservation biology, the candidate should be committed to doing field work, wet-lab work, learning
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted to complex 3D underground geometries. The research will be conducted in close
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of interest include, but are not limited to, biotic interactions, community ecology, conservation biology, ecological modelling, and eco-evolutionary feedback loops. The successful candidate will develop
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of mathematics, computer science, and evolutionary biology. We develop methods to understand evolutionary, ecological, epidemiological, and developmental processes on different scales based on genetic data. In our
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postdoctoral researcher with a strong background in sequence bioinformatics, algorithms and data structures. The successful candidate will join an interdisciplinary effort developing innovative diagnostic
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: developing systems where algorithmic decisions can be traced and audited; ensuring that outcomes are verifiable against standards and rules; (ii) transparency and explainability: creating interpretable models
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are looking for highly motivated applicants with a master's degree in evolutionary biology, population genetics, or conservation genetics. In addition to an interest in evolutionary and conservation biology
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework