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Field
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lab’s website https://sites.duke.edu/corinnelinardiclab/ . Be You Work Performed · Perform literature reviews to guide research · Design and execution of standard in vitro assays ongoing in the lab
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; Machine Learning• Clinical pathways and decision support for patients with acute chest pain• AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis• Speaking
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integrating biomedical, epidemiological, or environmental data. Must show solid skills in computational modeling, multivariate statistics, and/or machine learning. Proven proficiency in the English language
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g
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apply with no application fee during Free Application Week, October 6–10, 2025.👉 Learn more and apply here: https://go.ua.edu/4g4YUyJOur faculty are advancing knowledge and innovation across four dynamic
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for Artificial Intelligence. https://miai.univ-grenoble-alpes.fr/ Activities Develop and evaluate deep learning tools for MRI fingerprint data Write scientific articles Present results at international conferences
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Learning Your role and goals Trustworthy & Adversarial Computing Lab (https://taclab.aalto.fi ) led by Sebastian Szyller is looking for a doctoral researcher (PhD student) to pursue a degree in trustworthy
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, etc.), and data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by
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machine learning, deep learning, or computer vision Experience with Python and common AI frameworks (PyTorch, TensorFlow) Interest in hallucination detection, robustness, trustworthiness, and (optionally
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, MONAI) Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not