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.

I am hiring postdocs to my group in Helsinki, in combinations of the following: foundations of (probabilistic) machine learning, beyond current distribution shift models, AI-assistance with computationally rational user models, particularly attractive applications in synthetic biology and biodesign. FCAI is looking for Postdocs, Research fellows, and PhD students in Machine learning and AI. Finnish Center for Artificial Intelligence FCAI and ELLIS Unit Helsinki are looking for several postdocs, research fellows and PhD students to join us in creating new machine learning techniques – your work can be theoretical or applied, or both. We have opportunities in the following areas of research: Reinforcement learning, Probabilistic methods, Simulation-based inference, Privacy-preserving machine learning, Collaborative AI and human modeling, Machine learning for science.

Postdocs in Helsinki: Topic: combinations of the following: - foundations of (probabilistic) machine learning - beyond current distribution shift models - AI-assistance with computationally rational user models - particularly attractive applications in synthetic biology and biodesign. Postdoc or research fellow in Manchester, UK: Develop new principles and probabilistic machine learning methods for Advanced User Modelling, sequential decision making and Automatic Experimental Design, with and without a Human-in-the-Loop. PhD students in Manchester, UK. FCAI is looking for Postdocs, Research fellows, and PhD students in Machine learning and AI. Finnish Center for Artificial Intelligence FCAI and ELLIS Unit Helsinki are looking for several postdocs, research fellows and PhD students to join us in creating new machine learning techniques – your work can be theoretical or applied, or both. We have opportunities in the following areas of research: 1) Reinforcement learning 2) Probabilistic methods 3) Simulation-based inference 4) Privacy-preserving machine learning 5) Collaborative AI and human modeling 6) Machine learning for science.