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hold, or be close to completion of, a relevant PhD/DPhil in one of the following subjects: computational genomics, genetic or molecular epidemiology, medical statistics or statistical genetics. You must
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or a closely related field (PhD candidates who have submitted or are about to submit their thesis will be considered) Experience of machine learning frameworks (e.g. TensorFlow) Knowledge of Python and C
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. DVXplorer), and tactile/force sensors. Strong background in computer vision and deep learning, with practical implementation experience. Proficiency in programming with C++ and Python, including use of ROS
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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-based cameras (e.g. DVXplorer), and tactile/force sensors. 3. Strong background in computer vision and deep learning, with practical implementation experience. 4. Proficiency in programming with
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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hold, or are close to completing, a PhD in robotics, robot learning, or a closely related field. You possess strong expertise in deep learning and robot navigation, with hands-on experience in deploying
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to develop a holistic e-motor cooling technology, maximising heat transfer through direct-contact, spray cooling. The Team is looking to appoint one Postdoctoral Research Associate on Machine-Learning Assisted
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no.: 4644 Explore and teach at the University of Vienna, where over 7,500 academic minds have found a unique blend of freedom and support. Join us if you're driven by a passion for top-notch international