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: The participant will learn or add to skills/expertise in: Handling forest plot data, Modeling wildland fire, Modeling prescribed fire and wildfire emissions, and; Modeling tree mortality from prescribed fire and
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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Models to push the frontier where computer vision, physics simulation, and embodied AI converge. Join Us! This position is part of a collaborative research programme between the University of Amsterdam
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profile and an interest in developing new AI models for high-dimensional biological data. You should have a solid foundation in areas such as machine learning, applied mathematics, statistics
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records, aiming to co-create practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in machine learning, neuroscience
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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work on the molecular genetics of age-related hearing loss (https://www.grilletlab.com/). The Grillet lab has generated a mouse model carrying a polymorphism identified in Genome Wide Association Studies
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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine