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Postdoc in eDNA technology for early detection of forest pathogens Umea Department of Forest Bioeconomy and Technology We are seeking a motivated postdoctoral researcher to develop and apply
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in defending future generation networks by: prevention of cyberthreats through anticipating and mitigating them, accurate detection of ongoing attacks with well-understood AI methods used for system
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Bayesian modelling methods, to investigate how Arctic plant communities and their functional composition have changed over time and in response to warming, precipitation change, and other environmental
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deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are
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outcomes in international conferences and scientific papers. It is advantageous if you have knowledge and experience of: - stormwater management, Bayesian statistics, sediment transport processes
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sequencing and other NGS-based data from patients, including mutation calling and detection of mosaic mutations. The work may also include statistical analyses and data visualization to identify and
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whole-genome sequencing and other NGS-based data from patients, including mutation calling and detection of mosaic mutations. The work may also include statistical analyses and data visualization to
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computational methods for decomposing HD-sEMG into motor-unit activity, combining sparse optimisation, variational inference, and Bayesian modelling with physiological constraints. These efforts are
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, for example in snow, fog, low-texture scenes and with unreliable GNSS or magnetometer data - Sensor health monitoring and fault detection, so that the autonomy systems can detect when a sensor can
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, retrieval-augmented generation, explainable AI, anomaly detection, or decision-support systems; has a strong publication record relative to career stage; has the ability to conduct independent research