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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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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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: 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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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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with evidence of other captures in other fungal species. We seek to understand the evolutionary history and horizontal transfer of the ToxTA transposon in natural populations of all fungal species in
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independently by two different Starship transposons, Sanctuary and Horizon with evidence of other captures in other fungal species. We seek to understand the evolutionary history and horizontal transfer
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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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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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looking for a skilled Research Engineer to develop and deploy cutting-edge reinforcement learning (RL) and imitation learning (IL) algorithms on real-world robot platforms. The project requires hands