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division, Department of Information Technology, and is carried out together with researchers who develop methods in deep learning and image analysis with applications in life sciences and medicine
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degree in computer science / mathematics /telecommunications / automatics, when starting the PhD. Programming: - Python language (required) - Deep Learning libraries (like TensorFlow, Keras, PyTorch
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of Neurology (https://movementdisorderslab.umn.edu/ ). The postdoctoral researcher will be involved in the execution of experiments examining the effects of globus pallidus deep brain stimulation on motor and
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beyond the PhD in a research or engineering environment focused on large-scale AI. Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3
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analytical findings clearly Experience with advanced forecasting models (e.g., machine learning or deep learning approaches for time series) Familiarity with working on public safety or social science datasets
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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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, and deployability of deep learning models on resource-constrained edge platforms. The PhD candidate will collaborate closely with international project partners and contribute to advancing next
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project at the forefront of microscopy and bioimage analysis (https://www.msca-agile.eu/ ) and contribute to OMERO integration and optimization of Biom3d, a cutting-edge deep-learning framework for 3D image
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learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in collaboration
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related e.g. to the biodiversity crisis, GPS independent navigation, and quantum sensing at ambient temperature. For more information see https://navisense.org/. The Biomimetic robotics research in your PhD