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microscopy, tissue culture, biochemistry, physiology, or biophysics. Enthusiasm and commitment to pursue novel research areas and develop new techniques and approaches. Team player with an interest in learning
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no.: 6225 Explore and teach at the University of Vienna, where over 7,700 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate about groundbreaking international
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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networks, from a machine learning and information theory perspective. This basic research project has strong translational potential and aims to elucidate how immune function is altered during sepsis, with
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Postdoc in Application of eXtended Reality for Inclusive Automated Vehicle and Road User Interaction
, supportive environment that fosters learning and professional growth. Job requirements Hold a PhD degree in a relevant field (e.g., Transportation, Human-Computer Interaction, Computer Science, or a closely
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encouraged to apply for the position. Aalborg University has a policy of parallel language use. International staff are encouraged, with support from the university, to learn Danish during their employment in
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, understand, and communicate in English sufficiently to perform job duties. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, and
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and security challenges, build sustainable solutions, and provide expert perspective on using AI. Required Qualifications - PhD in computer science, statistics, data analytics, business administration
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.