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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.