1) Lecturer/Senior Lecturer (Assoc/Asst Prof) in Machine Learning: The University of Manchester is making a strategic investment in fundamentals of AI, to complement its existing strengths in AI applications across several prominent research fields in the University. Applications are welcome in any area of the fundamentals of machine learning, in particular probabilistic modelling, deep learning, reinforcement learning, causal modelling, human-in-the-loop ML, explainable AI, ethics, privacy and security. This position is meant to contribute to machine learning methodologies and not purely to their applications. You will be located in the Department of Computer Science and, in addition to the new centre for Fundamental AI research, you will belong to a large community of machine learning, data science and AI researchers. 2) Programme Manager – Centre for AI Fundamentals: The University of Manchester is seeking to appoint an individual with a strategic mindset and a track record of building and leading collaborative relationships and professional networks, expertise in a domain ideally related to artificial intelligence, excellent communication and interpersonal skills, experience in managing high-performing teams, and demonstrable ability to support the preparation of large, complex grant proposals to take up the role of Programme Manager for the Centre for AI Fundamentals. The successful candidate will play a major role in developing and shaping the Centre, working closely with its Director to grow the Centre and plan and deliver an exciting programme of activities, including leading key science translational activity and development of use cases in the Centre’s key domains, partnership development, bid writing, resource management, impact and public engagement strategies.

The Gatsby Unit seeks to appoint a new principal investigator with an outstanding record of research achievement and an innovative research programme in theoretical neuroscience or machine learning at any academic rank. In theoretical neuroscience, we are particularly interested in candidates who focus on the mathematical underpinnings of adaptive intelligent behaviour in animals, or develop mathematical tools and models to understand how neural circuits and systems function. In machine learning, we seek candidates who focus on the mathematical foundations of learning from data and experience, addressing fundamental questions in probabilistic or statistical machine learning and understanding; areas of particular interest include generative or probabilistic modelling, causal discovery, reinforcement learning, theory of deep learning, and links between these areas and neuroscience or cognitive science.

The EDUCADO project (Exploring the Deep Universe by Computational Analysis of Data from Observations) offers a unique opportunity for early stage researchers to develop into mature interdisciplinary scientists. You will be part of a network of 9 research groups across Europe. The formation and evolution of massive galaxies is reasonably well understood in the context of the successful standard ΛCDM formalism. Such simulations of cosmic evolution, however, lead to serious challenges in the regime of very faint galaxies (problems referred to as missing satellites, too big to fail, and planes of satellite galaxies). With the massive amounts of excellent data being produced by astronomical surveys, and with new missions scheduled to produce more data of even better quality, we have a unique chance to solve these problems. Linking detailed astrophysical simulations with observations requires development of dedicated tools in the frameworks of machine learning, natural computation and probabilistic modelling.

Manchester: Postdoc in probabilistic machine learning and sustainability; collaboration with an outstanding sustainability team - Univ Manchester is top in the UK and Europe, and 3rd in the world in the QS World University Ranking for Sustainability. This position belongs to the European Lighthouse of AI for Sustainability ELIAS. Helsinki: Probabilistic modelling and Bayesian inference for Machine Learning, ML for drug design, synthetic biology and biodesign, with differential privacy, for personalized medicine, for next-generation distribution shifts, or for collaborative machine learning.

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.