The University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen are two of the world’s leading centres for machine learning research. In 2014, we launched a new and exciting initiative whereby a small group of select PhD candidates are jointly supervised at both institutions. The principal supervisors are Carl Rasmussen, Neil Lawrence, Ferenc Huszar, Jose Miguel Hernandez-Lobato, David Krueger, Adrian Weller and Rika Antonova at Cambridge University, and Bernhard Schölkopf and other research group leaders at the Max Planck Institute in Tübingen. This program is specific for candidates whose research interests are well-matched to both the principal supervisors in Cambridge and the MPI for Intelligent Systems in Tuebingen. The overall duration of the PhD will be four years, with roughly three years spent at one location, and one year spent at the other location, including initial coursework at the University of Cambridge. Successful PhDs will be officially granted by the University of Cambridge.

Biological systems learn differently than current machine learning systems, with generally higher sample efficiency but also strong inductive biases. The scientist will explore the effects which bio-realistic neurons, plasticity rules and architectures have on learning in artificial neural networks. This will be done by combining construction of artificial neural network with bio-inspired constraints.

The opening of a PhD position on the Topic of Machine Learning with Missing Features, as part of a newly established research project of the machine learning group at Bielefeld University and the Honda Research Institute (HRI) Europe in Offenbach. The aim is the development of machine learning methods that are suitable for variable or systematically sparse input features. Examples include models for personal data with partial information or technical applications with varying sensor equipment.

We are looking for an enthusiastic new colleague to come work with us on fundamental topics in machine learning.

Passionate PhD and Postdoc candidates are sought for research on machine learning for graph and temporal data. The research topics can be found at the provided project link.

This Ph.D. position is focused on machine learning in realistic settings referring to statistical and system characteristics such as robustness to limited data and distribution shifts. For the application side, the candidate will collaborate with the Institute of Marine Research on valuable image data of the marine environment.

Thinking about the next position for your research career? I am hiring postdocs in my machine learning research group both in Helsinki, Finland and Manchester, UK. We develop new machine learning methods and study machine learning principles. Keywords include: probabilistic modelling, Bayesian inference, simulation-based inference, multi-agent RL and collaborative AI, sequential decision making and experimental design, active learning, human-in-the-loop learning and user modelling, privacy-preserving learning, Bayesian deep learning, generative models. We also solve problems of other fields with the methods – and use those problems as test benches when developing the methods. We have excellent collaborators in drug design, synthetic biology and biodesign, personalized medicine, cognitive science and human-computer interaction.

Thinking about the next position for your research career? I am hiring postdocs in my machine learning research group both in Helsinki, Finland and Manchester, UK. We develop new machine learning methods and study machine learning principles. Keywords include: probabilistic modelling, Bayesian inference, simulation-based inference, multi-agent RL and collaborative AI, sequential decision making and experimental design, active learning, human-in-the-loop learning and user modelling, privacy-preserving learning, Bayesian deep learning, generative models. We also solve problems of other fields with the methods – and use those problems as test benches when developing the methods. We have excellent collaborators in drug design, synthetic biology and biodesign, personalized medicine, cognitive science and human-computer interaction.

The University of Bath invites applications for a fully-funded PhD position in Machine Learning, as part of the prestigious URSA competition. This project focuses on developing interpretable machine learning methods for high-dimensional data, with an emphasis on recognizing symmetries and incorporating them into efficient, flexible algorithms. This PhD position offers the opportunity to work within a leading research environment, using state-of-the-art tools such as TensorFlow, PyTorch, and Scikit-Learn. The research outcomes have potential applications in diverse fields, and students are encouraged to bring creative and interdisciplinary approaches to problem-solving.

Talks and panel discussions around the LifeQ process of moving from the embedded engineering of sensors on edge devices to big health data analysis in the cloud.

Artificial intelligence (AI) and machine learning (ML) can facilitate new paradigms and solutions in almost every research field. Collaboration is essential to achieve tangible and concrete progress in impactful and meaningful AI and ML research, due to its transdisciplinary nature. Come and meet University of Pretoria (UP) academics that are embracing and exploring the opportunities that AI and ML offer to transcend the conventional boundaries of their disciplines. Join the discussion to debate this new frontier of opportunities and challenges that may enable you to look beyond the obvious, and discover new directions and opportunities that we may offer for tomorrow — together!

What does an executive at a South African Bank, a machine learning lead, and a CEO of an AI company have in common? They all will be on a panel talking about careers in Data Science, Machine Learning and Artificial Intelligence

This is my C-14 Impaler gauss rifle! There are many like it, but this one is mine!" - A terran marine If you have never heard of a terran marine before, then you have probably missed out on playing the very engaging and entertaining strategy computer game, StarCraft. However, don’t despair, because what we have in store might be even more exciting! In this interactive session, we will take you through, step-by-step, on how to train a team of terran marines to defeat a team of marines controlled by the built-in game AI in StarCraft II. How will we achieve this? Using multi-agent reinforcement learning (MARL). MARL is a useful framework for building distributed intelligent systems. In MARL, multiple agents are trained to act as individual decision-makers of some larger system, while learning to work as a team. We will show you how to use Mava (https://github.com/instadeepai/Mava), a newly released research framework for MARL to build a multi-agent learning system for StarCraft II. We will provide the necessary guidance, tools and background to understand the key concepts behind MARL, how to use Mava building blocks to build systems and how to train a system from scratch. We will conclude the session by briefly sharing various exciting real-world application areas for MARL at InstaDeep, such as large-scale autonomous train navigation and circuit board routing. These are problems that become exponentially more difficult to solve as they scale. Finally, we will argue that many of humanity’s most important practical problems are reminiscent of the ones just described. These include, for example, the need for sustainable management of distributed resources under the pressures of climate change, or efficient inventory control and supply routing in critical distribution networks, or robotic teams for rescue missions and exploration. We believe MARL has enormous potential to be applied in these areas and we hope to inspire you to get excited and interested in MARL and perhaps one day contribute to the field!

Tutorial on Notebook workflows for reproducible data science.

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.

A panel discussion on "What might we still require to achieve AGI?", a set of Reinforcement Learning and Computer Vision domain tuts and a talk from George Konidaris

Have a great idea involving AI? Want to launch your own business? It takes many iterations before an idea becomes a startup. Lots of coffee, heartache, and git reverts fuel these iterations. We have learned a lot from Cape AI's own incubated startup, Moonshop, Africa's first autonomous microstore. Watch the demo here: https://www.youtube.com/watch?v=odX6kxhLFC4 Attend our virtual roadshow event to hear lightning talks on creating proofs of concept, failing fast, funding models, selecting and growing a team, finding customers/clients, and building your brand. Afterwards, there will be a short break, then a panel discussion where members of the Cape AI team will answer questions from the audience.