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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft
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transitions and universality for spectral statistics of random matrices and their applications in high-dimensional statistics, machine learning and probability theory. The Department of Mathematics at KTH
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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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at the intersection between analytical chemistry, chemometrics and life sciences. As a postdoc in this project you will learn to use and help to develop cutting-edge methodologies linked to vibrational spectroscopy and
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have