Topic: deep-learning models

VIB’s Center for AI and Computational Biology and KU Leuven’s Department of Human Genetics seek a group leader and professor developing AI and mechanistic mathematical models for biological questions. Potential themes include hybrid mechanistic and deep-learning models, genome-regulation foundation models, single-cell and spatial multi-omics, protein structure and interaction networks, imaging-based models of cells and tissues, and disease digital twins. The appointment includes salary, core research funding and access to research infrastructure. Responsibilities encompass building an independ

Build and evaluate deep-learning models for biological prediction using Python and Jupyter notebooks. Lectures and collaborative exercises develop convolutional networks for classification, regression and image segmentation, with attention to preprocessing, augmentation, class imbalance, cross-validation and overfitting. Later sessions explore learning-rate and training-duration choices, recurrent networks and applications to omics data. Participants interpret model performance and discuss their own research problems. 2026-11-23–2026-11-27, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). O

I describe a well-tested Python module that does automated alignment and warping of faces images, and some advantages over existing solutions. An additional tool I’ve developed does automated extraction of facial features, which can be used in a number of interesting ways. I illustrate the value of wavelet-based features with a brief description of 2 recent studies: perceptual in-painting, and the robustness of the whole-part advantage across a large stimulus set. Finally, I discuss the suitability of various deep learning models for generating stimuli to study perceptual face spaces. I belie