We offer a postdoctoral researcher position within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, The Netherlands. The position is funded by an NWO Vidi project named “mechanistic machine learning: combining the explanatory power of dynamic models with the predictive power of machine learning“. Systems of Artificial Intelligence (AI) and Machine Learning (ML) gained a tremendous amount of interest in recent years, demonstrating great performance for a wide variety of tasks, but typically only if they are trained on huge amounts of data. Moreover, frequently no insight into the decision making is available or required. Experts desire to know how their data can inform them about the natural processes being measured. Therefore we develop transparent and interpretable model- and data-driven hybrid methods that are demonstrated for applications in medicine and engineering. As a postdoc, you will work together with Kerstin Bunte and her team within the Intelligent Systems group, as well as a network of interdisciplinary collaborators in the UK and Europe from various fields, such as Computer Science, Engineering and Applied Mathematics.

Two new Assistant Professor positions available at Lancaster University Leipzig, one with a focus on data science, one focussed on cyber security. Lancaster University Leipzig is a young branch campus of Lancaster University, based in Leipzig Germany. The department is focussed on intelligent systems, including topics such as emergent behaviour, unconventional computing, quantum software engineering, and robotics. These are grade 8 openings, so we would expect to see a research plan and teaching experience from interested applicants (grade 7 is the typical entry-level asst. prof. level).

The Department of Informatics at the University of Oslo, Norway is looking for candidates to fill two permanent positions as Associate Professors in Machine Learning. The positions can be affiliated to or interact with the Robotics and Intelligent Systems (ROBIN) group at the University. Candidates with a background in artificial intelligence/machine learning related to robotics or embedded systems are encouraged to apply. The candidates will be evaluated with respect to two different profiles: 1. Associate Professor in Ethical Considerations in Machine Learning: For this position, we are looking for a candidate with a research background in machine learning including applications and a track record in analysing aspects of machine learning methodology related to ethical considerations. 2. Associate Professor in Machine Learning: This position is expected to be offered to a candidate with a strong research background in machine learning including applications. Please note that this position is announced in Norwegian and with a requirement for candidates to have fluent oral and written communication skills in both English and a Scandinavian language.

The Max Planck Institute for Intelligent Systems and the Universities of Stuttgart and Tübingen collaborate to offer an interdisciplinary doctoral program, the International Max Planck Research School for Intelligent Systems (IMPRS-IS). This doctoral program will accept its ninth generation of Ph.D. students in spring of 2024. This school is a key element of Baden-Württemberg’s Cyber Valley initiative to accelerate basic research and commercial development in artificial intelligence and robotics. We seek students who want to earn a doctorate while contributing to world-leading research in areas such as Artificial Intelligence, Biomedical Technology, Computational Cognitive Science, Computer Vision and Graphics, Control Systems and Optimization, Data Science & Visualization, Haptics and Human-Computer Interaction, Machine Learning, Micro- and Nano-Robotics, Natural Language Processing, Neuroscience, Perceptual Inference, Robotics and Human-Robot Interaction, Soft Robotics and Materials. Admitted students can join our program starting in spring of 2025. You will be mentored by our internationally renowned faculty. You will register as a university doctoral student and conduct research. IMPRS-IS offers a wide variety of scientific seminars, workshops, and social activities. All aspects of our program are in English. Your doctoral degree will be conferred when you successfully complete your doctoral project. Our dedicated staff members will assist you throughout your time as a doctoral student.

The Max Planck Institute for Intelligent Systems and the Universities of Stuttgart and Tübingen collaborate to offer an interdisciplinary Ph.D. program, the International Max Planck Research School for Intelligent Systems (IMPRS-IS). This doctoral program will accept its eighth generation of Ph.D. students in spring of 2024. This school is a key element of Baden-Württemberg’s Cyber Valley initiative to accelerate basic research and commercial development in artificial intelligence and robotics. We seek students who want to earn a doctorate while contributing to world-leading research in areas such as: Biomedical Technology, Computational Cognitive Science, Computer Vision and Graphics, Control Systems and Optimization, Data Science, Haptics and Human-Computer Interaction, Machine Learning, Micro- and Nano-Robotics, Neuroscience, Perceptual Inference, Robotics and Human-Robot Interaction, Soft Robotics and Materials.

Prof. Angela Yu recently moved from UCSD to TU Darmstadt as the Alexander von Humboldt AI Professor, and has a number of PhD and postdoc positions available in her growing “Computational Modeling of Intelligent Systems” research group. Applications are solicited from highly motivated and qualified candidates, who are interested in interdisciplinary research at the intersection of natural and artificial intelligence. Prof. Yu’s group uses mathematically rigorous and algorithmically diverse tools to understand the nature of representation and computations that give rise to intelligent behavior. There is a fair amount of flexibility in the actual choice of project, as long as the project excites both the candidate and Prof. Yu. For example, Prof. Yu is currently interested in investigating scientific questions such as: How is socio-emotional intelligence similar or different from cognitive intelligence? Is there a fundamental tradeoff, given the prevalence of autism among scientists and engineers? How can AI be taught socio-emotional intelligence? How are artificial intelligence (e.g. as demonstrated by large language models) and natural intelligence (e.g. as measured by IQ tests) similar or different in their underlying representation or computations? What roles do intrinsic motivations such as curiosity and computational efficiency play in intelligent systems? How can insights about artificial intelligence improve the understanding and augmentation of human intelligence? Are capacity limitations with respect to attention and working memory a feature or a bug in the brain? How can AI system be enhanced by attention or WM? More broadly, Prof. Yu’s group employs and develops diverse machine learning and mathematical tools, e.g. Bayesian statistical modeling, control theory, reinforcement learning, artificial NN, and information theory, to explain various aspects of cognition important for intelligence: perception, attention, decision-making, learning, cognitive control, active sensing, economic behavior, and social interactions. Participants who have experience with two or more of the technical areas, and/or one or more of the application areas, are highly encouraged to apply. As part of the Centre for Cognitive Science at TU Darmstadt, the Hessian AI Center, as well as the Computer Science Department, Prof. Yu’s group members are encouraged and expected to collaborate extensively with preeminent researchers in cognitive science and AI, both nearby and internationally. All positions will be based at TU Darmstadt, Germany. Starting dates for the positions are flexible. Salaries are commensurate with experience and expertise, and highly competitive with respect to U.S. and European standards. The working language in the group and within the larger academic community is English. Fluency in German is not required; the university provides free German lessons for interested scientific staff.

The School of Information Science and Technology (IST) at Vidyasirimedhi Institute of Science and Technology (VISTEC), Thailand, offers a full-time position (Professor / Associate Professor / Assistant Professor / Lecturer (Tenure Track)). We are expanding our faculty to support the Sense-Think-Act initiative, which focuses on the next generation of AI-driven systems capable of perceiving their environment, reasoning effectively, and acting autonomously. We seek faculty members from diverse fields, including Computer Science, Computer Engineering, Software Engineering, Robotics, and Biomedical Engineering, to drive innovation in AI, robotics, and intelligent systems.

The Utrecht University offers a fully paid 4-year PhD position on causality-aware explanations for probabilistic graphical models. This project is a collaboration between Intelligent Systems group at Utrecht University and Leiden Institute of Advanced Computer Science and will be part of the Hybrid Intelligence Centre. The position falls under the Collective Labour Agreement of Dutch Universities (solid pension scheme, substantial holiday leave, paid sick leave, maternity/paternity leave) with a gross monthly salary between €2,901 and €3,707, 8% holiday pay and 8.3% year-end bonus.

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!

Designing artificially intelligent systems and interfaces with socio-emotional skills is a challenging task. Progress in industry and developments in academia provide us a positive outlook, however, the artificial social and emotional intelligence of the current technology is still limited. My lab’s research has been pushing the state of the art in a wide spectrum of research topics in this area, including the design and creation of new datasets; novel feature representations and learning algorithms for sensing and understanding human nonverbal behaviours in solo, dyadic and group settings; designing longitudinal human-robot interaction studies for wellbeing; and investigating how to mitigate the bias that creeps into these systems. In this talk, I will present some of my research team’s explorations in these areas including social appropriateness of robot actions, virtual reality based cognitive training with affective adaptation, and bias and fairness in data-driven emotionally intelligent systems.