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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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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
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for disease localization without requiring pixel-level annotations. The work spans several core areas: machine learning, computer vision, deep learning, and medical imaging. The successful candidate will design
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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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assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
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combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position for you! About us Our team is part of the Division
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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requirement. A very good command of the English language, both written and spoken, is a key requirement. Experience in Federated Learning, Computer Vision, Image Analysis, Mathematics, and Mathematical