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/expression conservation and the mechanistic modeling. You will develop machine learning/deep learning-based bioinformatics methods to translate gene regulation modules between species by integrating orthology
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through even the most complex structures and materials, enabling microscopy deep inside the organ on chip systems. This technique was invented at the University of Twente, and this project is part of our
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Postdoc in Machine Learning for Oligopeptide Design (1.0 FTE) (V24.0584) « Back to the overview Job description The advent of modern machine learning (ML) methodology is accelerating scientific
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you will extend this work to the ultrasonic sounds of bats, insects and other animals. This involves curating open datasets of sound, as well as training deep learning, and validating that these methods
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, Biomedical Engineering, or related field; You demonstrate strong programming skills in Python and have experience with deep learning frameworks such as PyTorch; You have experience in medical image analysis
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that integrate deep learning with neuroscientific mechanisms to understand auditory scene processing. You will design, implement, and evaluate AI models for auditory scene analysis, working with large-scale sound
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difference-frequency generation (IDFG) based sources extend deep into the mid-infrared wavelength range with unprecedented spectral coverage (2–11.5 µm). In our lab, we have recently demonstrated a system
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explaining variables. The postdoc researcher will work on (1) collecting images at relevant study sites in Amsterdam, (2) designing and training AI-based models using deep learning, and (3) identifying and
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validating existing deep learning models for prediction of molecular subtypes on real-world HR-NMIBC cohorts. This will involve research into the use of state-of-the-art weakly-supervised and explainable AI
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validating existing deep learning models for prediction of molecular subtypes on real-world HR-NMIBC cohorts. This will involve research into the use of state-of-the-art weakly-supervised and explainable AI