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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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scenarios. The research combines field experiments, AI-based analysis of museum specimens, and advanced climate modeling to provide process-based insights into the ecological and economic consequences
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unexplored intersection between social cognition, attentional processing, and gaze perception. We use several different methods, including functional magnetic brain imaging (fMRI), behavioral and
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the underlying principles. For us, it is equally important to study the impact of materials on biological processes as well as the impact of biological processes on materials. Our ambition is to foster a dynamic
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and project developers in reusing them in new projects. Taking the Chalmers campus as a starting point, we are developing scalable, AI-powered methods, such as computer vision for street-view imagery
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transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence
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systems for diagnostics and treatment. Core activities include signal processing, antenna design, and measurement hardware development. Building complete prototype systems for clinical testing is a central
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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new ways of processing information - far beyond the limits of classical systems. Our research spans quantum computing, sensing, transduction, thermodynamics, and foundations, all aimed at harnessing