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PhD position (3 years) Participation in an international research collaboration Access to cutting-edge technologies, including spatial transcriptomics and advanced imaging A dynamic and collaborative
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Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of
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across conditions and integrate phenotypic readouts with functional omics to identify the processes underlying key performance changes. You will work closely with a postdoc evolving these consortia and
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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque electrophysiology data obtained through
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statistical methods to large-scale medical datasets (EHR, imaging, genomics, clinical trials). Design algorithms and predictive models to advance diagnostics, medical devices, healthcare access, and
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young versus older adults using EEG. Analyses will focus on Event-Related Potentials, time-frequency analysis and functional connectivity measures within a cognitive-motor dual task paradigm. To enhance
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 5 days ago
of graph databases, prototype systems have shown how property graph views can be defined, materialized, and incrementally updated over underlying storage layers ([Han24]). However, existing techniques stop
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training, exchanges, and career development for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and
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visiting academics, PhD students, and postdocs who are travelling to Munich for a set period of time to find accommodation. For further information, please visit the following link: https://www.lmu.de/en