Topic: PyTorch

A two-day live course introduces fully connected neural networks, convolutional networks for computer vision, recurrent networks for text and speech, transfer learning and large language models. Participants use PyTorch to build and train models on high-end GPUs through an HPC cluster, connecting from their own computers to the Austrian Scientific Computing JupyterHub. Temporary cluster accounts support the exercises, and a short pre-assignment prepares participants before the course. The English-language course is intended for software developers, data scientists and researchers moving from

Implement and assess coupled stochastic variational models of ocean circulation within the ERC STUOD project and Odyssey team. The research uses modelling under location uncertainty to represent unresolved flow dynamics while retaining physically justified properties. Numerical experiments will examine coupled ocean flows, small-scale dynamics and wave-current interactions in idealized settings. Required background includes applied mathematics, fluid dynamics, stochastic parameterization, Fortran, Python and PyTorch. Appointment: 6 months; advertised start 2026-12-01. Starting from €2,695 gros

We introduce Norse: An open-source library for gradient-based training of spiking neural networks. In contrast to neuron simulators which mainly target computational neuroscientists, our library seamlessly integrates with the existing PyTorch ecosystem using abstractions familiar to the machine learning community. This has immediate benefits in that it provides a familiar interface, hardware accelerator support and, most importantly, the ability to use gradient-based optimization. While many parallel efforts in this direction exist, Norse emphasizes flexibility and usability in three ways. Use

Norse aims to exploit the advantages of bio-inspired neural components, which are sparse and event-driven - a fundamental difference from artificial neural networks. Norse expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components.

In this workshop you'll learn the fundamentals of PyTorch using an incremental, from-first-principles approach. We'll start with tensors, autograd, and the dynamic computation graph, and then move on to developing and training a simple model using PyTorch's model classes, datasets, data loaders, optimizers, and more. You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.