The Department of Engineering Mathematics at the University of Bristol is seeking an outstanding candidate to fill the role of Professor in Artificial Intelligence. You will have the opportunity to provide visionary leadership to the department and its staff, students, & partners, helping to strengthen and further develop our already impressive research and teaching programs in AI. Our Intelligent Systems Group supports the Faculty of Engineering's AI/Data Science Theme, fostering an inclusive environment for all.

The job is a W3 Full Professorship for Artificial Intelligence in interactive Systems at Technische Universität Braunschweig. The role involves expanding the research area of data-driven methods for interactive and intelligent systems at the TU Braunschweig and strengthening the focal points 'Data Science' and 'Reliability' of the Department of Computer Science. The position holder is expected to have a strong background in Computer Science with a focus on Artificial Intelligence/​Machine Learning, specifically in the areas of Dependable AI and Explainable AI. The role also involves teaching, topic-related courses in the areas of Artificial Intelligence and Machine Learning to complement the Bachelor's and Master's degree programs of the Department of Computer Science.

This is a university doctoral position that involves minor teaching duties. The precise research topics are negotiable within the scope of active research at IIS, including machine learning and growing levels of AI for computer vision and robotics. Of particular interest are topics in representation learning and causality for out-of-distribution situations.

The position holder will be a member of the Hessian Center for Artificial Intelligence - hessian.AI and provides research at the Center and will also be a member of the Centre for Cognitive Science. The scientific focus of the position is on the computational and algorithmic modeling of behavioral data to understand the human mind. Exemplary research topics include computational level models of perception, cognition, decision making, action, and learning as well as extended behavior and social interactions in humans, algorithmic models that are able to simulate, predict, and explain human behavior, model-driven behavioral research on human cognition. The professorship is expected to strengthen the Hessian Center for Artificial Intelligence and TU Darmstadt’s Human Science department’s research focus on Cognitive Science. Depending on the candidate’s profile there is the opportunity to participate in joint research projects currently running at TU Darmstadt. This in particular includes the state funded cluster projects “The Adaptive Mind (TAM)” and “The Third Wave of Artificial Intelligence (3AI)”. In addition to excellent scientific credentials, we seek a strong commitment to teaching in the department’s Bachelor and Masters programs in Cognitive Science. Experience in attracting third-party funding as well as participation in academic governance is expected.

We are seeking highly qualified and motivated individuals for the positions of Assistant and Associate Professors in Artificial Intelligence (AI) and Machine Learning (ML). The successful candidate will join our esteemed faculty in the Institute for Advancing Intelligence (IAI), TCG Centre for Research and Education in Science and Technology (CREST), Kolkata, India, and contribute to our commitment to excellence in research, teaching, and academic services. TCG CREST has set up the campus in Sector V, Salt Lake City, Kolkata, India. State-of-the-art laboratories and research facilities for the individual Institutes, spacious classrooms and technology interventions for executing both off-line and on-line academic classes and programs, conference rooms, and other infrastructures provide the students and the faculty an ideal environment for creative exchanges and high-end research collaborations.

The AI Department of the Donders Centre for Cognition (DCC), embedded in the Donders Institute for Brain, Cognition and Behaviour, and the School of Artificial Intelligence at Radboud University Nijmegen are looking for a researcher in reinforcement learning with an emphasis on safety and robustness, an interest in natural computing as well as in applications in neurotechnology and other domains such as robotics, healthcare and/or sustainability. You will be expected to perform top-quality research in (deep) reinforcement learning, actively contribute to the DBI2 consortium, interact and collaborate with other researchers and specialists in academia and/or industry, and be an inspiring member of our staff with excellent communication skills. You are also expected to engage with students through teaching and master projects not exceeding 20% of your time.

The Institute for Neural Computation is looking for a postdoc in the field of Computational Neuroscience. The position is part of the group 'Theory of Neural Systems' and offers the opportunity to develop your own research profile and establish an independent research group. The research topic should be in the field of computational neuroscience on a system level, in particular modeling the visual system, episodic memory, or navigation in mammals. Collaborations with colleagues at the institute are welcome. The tasks include independent research projects and publications, acquiring third party funding, teaching, supervising student projects and your own PhD projects, and active participation in the local research environment.

Open professorship in Industrial AI with a focus on Neurosymbolic Learning & Reasoning. The role involves shaping research and teaching in the field of neurosymbolic approaches for industrial applications internationally and forming our environment in a lasting way.

Applications are invited for an academic position in machine learning in the School of Informatics at the University of Edinburgh, as part of a continuing expansion in Machine Learning and Artificial Intelligence. The appointment will be full-time and open-ended. The successful candidate will have (or be near to completing) a PhD, an established research agenda and the enthusiasm and ability to undertake original research, and to lead a research group. They will show excellent teaching capability and engagement with academic supervision. We are seeking current and future leaders in the field. We seek candidates with research interests in the development of cutting-edge machine learning methods. Candidates will have a research interests in principled approaches to machine learning, machine learning for novel or critical applications, and/or the development of novel methods of wide applicability and with state-of-the-art capability.

The Robert J. and Nancy D. Carney Institute for Brain Science at Brown University invites applications from rising and established leaders in computational brain science for a tenure-track position at the Assistant Professor level. The successful applicant will have an outstanding record of research and teaching that contributes to the mission of the candidate’s appropriate home department and the Carney Institute Center for Computational Brain Science. We are particularly interested in scholars who demonstrate commitment to promoting diversity and inclusion in the brain sciences.

The Faculty of Engineering, Computer Science and Psychology, Institute of Neural Information Processing, is seeking to fill the position of Professor (W3) of Machine Learning. The research focus of this professorship should center around fundamental methodological contributions to machine learning. This includes expertise in statistical learning methods, neural network architectures, cognitive modeling of learning and adaptation processes, relational and structured learning models, and related topics. The ideal candidate will demonstrate connections to the faculty's key focus area, 'Cognitive Systems', and the strategic development area 'Data Science'. Moreover, affiliations to fields such as medical image processing, psychology of cognitive processes, technical adaptive systems, and relevant topics in other faculties of the university are desirable. Collaborative involvement with companies in the Science Park of Ulm and the newly founded DLR Institute for Secure AI is also welcome. Experience in acquiring third-party funding and participation in national and international research collaborations are required attributes. In teaching, the professorship will cover the area of 'Machine Learning' and practical and applied computer science topics in the core curriculum of the bachelor's degree programs in computer science. Teaching of advanced modules is, in particular, expected in the master's degree program 'Artificial Intelligence' and in the international master's degree program 'Cognitive Systems'. Modules at the master’s level are primarily taught in English. The candidate's excellence in teaching will be evident through appropriate teaching evaluations. Participation in academic administration is expected. The professorship is linked to the role of deputy director for the Institute of Neural Information Processing.

The Department of Biology at Washington University in St. Louis seeks a neuroscientist for a tenure-track position at the Assistant or Associate Professor level. The successful candidate will establish a research program focused on cutting-edge questions in developmental, cellular or systems neuroscience with particular interest in neuroethology, biologically-inspired artificial intelligence, evolution, or neural computation. The successful candidate will: join a vibrant neuroscience community; contribute to advising, mentoring, and teaching; and develop an externally funded and internationally recognized research program.

We are seeking highly qualified and motivated individuals for the positions of Assistant and Associate Professors in Artificial Intelligence (AI) and Machine Learning (ML). The successful candidate will join our esteemed faculty in the Institute for Advancing Intelligence (IAI), TCG Centre for Research and Education in Science and Technology (CREST), Kolkata, India, and contribute to our commitment to excellence in research, teaching, and academic services. The campus is set up in Sector V, Salt Lake City, Kolkata, India with state-of-the-art laboratories and research facilities for the individual Institutes, spacious classrooms and technology interventions for executing both off-line and on-line academic classes and programs, conference rooms, and other infrastructures provide the students and the faculty an ideal environment for creative exchanges and high-end research collaborations.

The Robert J. and Nancy D. Carney Institute for Brain Science at Brown University invites applications from rising and established leaders in computational brain science for a tenure-track/tenured position at the Assistant, Associate of Full Professor level. The successful applicant will have an outstanding record of research and teaching that contributes to the mission of the candidate’s appropriate home department and the Carney Institute Center for Computational Brain Science. We are particularly interested in scholars who demonstrate commitment to promoting diversity and inclusion in the brain sciences.

The Department of Psychology at UC San Diego invites applications for a tenure-track Assistant Professor position focused on computational and theoretical mechanisms of behavior and/or its neural bases. The selected candidate will be responsible for establishing a rigorous, high-quality research program that complements existing departmental strengths in Behavioral Neuroscience, Cognitive Psychology, Developmental Psychology, and/or Social Psychology. Additional responsibilities include teaching graduate and undergraduate level courses and mentoring students within the Department of Psychology, as well as participating in department and university service.

The Post Doc for Neuromarketing Based on Brain-Computer Interfaces and AI will be part of the “Career@BI” project, in cooperation with SNAP GmbH. The role involves enhancing your profile and qualifying for professorship at a university of applied sciences. You will gain practical professional experience in the university context and get to know the diverse tasks of university routines. You will add to your teaching and research experience, take part in qualification offers and thus enhance your profile for appointability as a professor at a university of applied sciences. You will also gain practical experience outside HSBI at SNAP GmbH. You will be part of an interdisciplinary team and contribute to the development of brain-computer interfaces (BCI), which are to be used for neuromarketing. The focus will be on software development and signal processing, primarily using AI methods. To collect data and validate the developed procedures, you will conduct studies with test persons.

Vector-space models are used frequently to compare similarity and dimensionality among entity concepts. What happens when we apply these models to relational concepts? What is the evidence that such models do apply to relational concepts? If we use such a model, then one implication is that maximizing surface feature variation should improve relational concept learning. For example, in STEM instruction, the effectiveness of teaching by analogy is often limited by students’ focus on superficial features of the source and target exemplars. However, in contrast to the prediction of the vector-space computational model, the strategy of progressive alignment (moving from perceptually similar to different targets) has been suggested to address this issue (Gentner & Hoyos, 2017), and human behavioral evidence has shown benefits from progressive alignment. Here I will present some preliminary data that supports the computational approach. Participants were explicitly instructed to match stimuli based on relations while perceptual similarity of stimuli varied parametrically. We found that lower perceptual similarity reduced accurate relational matching. This finding demonstrates that perceptual similarity may interfere with relational judgements, but also hints at why progressive alignment maybe effective. These are preliminary, exploratory data and I to hope receive feedback on the framework and to start a discussion in a group on the utility of vector-space models for relational concepts in general.

Analogies between old and new concepts are common during classroom instruction. While previous studies of transfer focus on how features of initial learning guide later transfer to new problem solving, less is known about how to best support analogical transfer from previous learning while children are engaged in new learning episodes. Such research may have important implications for teaching and learning in mathematics, which often includes analogies between old and new information. Some existing research promotes supporting learners' explicit connections across old and new information within an analogy. In this talk, I will present evidence that instructors can invite implicit analogical reasoning through warm-up activities designed to activate relevant prior knowledge. Warm-up activities "close the transfer space" between old and new learning without additional direct instruction.

Vocabulary learning applications like Duolingo have millions of users around the world, but yet are based on very simple heuristics to choose teaching material to provide to their users. In this presentation, we will discuss the possibility to develop more advanced artificial teachers, which would be based on modeling of the learner’s inner characteristics. In the case of teaching vocabulary, understanding how the learner memorizes is enough. When it comes to picking grammar exercises, it becomes essential to assess how the learner reasons, in particular by analogy. This second application will illustrate how analogical and case-based reasoning can be employed in an alternative way in education: not as the teaching algorithm, but as a part of the learner’s model.

Extensive research on science teaching has shown the effectiveness of analogies as a didactic tool which, when appropriately and effectively used, facilitates the learning process of abstract concepts. This seminar does not contradict the efficacy of such a didactic use of analogies in this seminar but switches attention and interest on their heuristic use in approaching and understanding of what previously unknown. Such a use of analogies derives from research with 10 to 17 year-olds, who, when asked to make predictions in novel situations and to then provide explanations about these predictions, they self-generated analogies and used them by reasoning on their basis. This heuristic use of analogies can be used in science teaching in revealing how students approach situations they have not considered before as well as the sources they draw upon in doing so.

The cerebellum resembles a feedforward, three-layer network of neurons in which the “hidden layer” consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

Studies reported the effects of different types and different levels of abstraction of analogical stimuli on designers. However, specific, single visual analogical stimuli on the effects of designers have not been reported. We define this type of stimuli as specific analogical stimuli. We used the extended linkography method to analyze the facilitating and limiting effects of specific analogical stimuli and free association analogical stimuli (nonspecific analogical stimuli) on the students' creativity at different design levels. Through an empirical study, we explored the differences in the effects of specific analogy stimuli on the students at different design levels. It clarifies the use of analogical stimuli in design and the teaching of analogical design methods in design education.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

Join us to hear about degrees and careers in neuroscience, what it’s like to be a neuroscientist, the wide range of career options open to you after a neuroscience degree, first-hand examples of career paths in neuroscience, and some tips and thoughts to help you in your own careers. This free and friendly webinar will give you the chance to ask questions from people with different experiences in neuroscience: - Emma Soopramanien, the BNA Committee Representative for Students and Early Career Researchers – Emma has just completed her undergraduate course in neuroscience, and will be hosting the webinar. - Professor Anthony Isles, BNA Trustee – Anthony is a professor at Cardiff University, where he researches epigenetic mechanisms of brain and behaviour and how they contribute to neurodevelopmental and neuropsychiatric disorders, as well as teaching undergraduate and postgraduate students. He will talk about how he came to be a neuroscientist researcher and ways into neuroscience. - Dr Anne Cooke, BNA Chief Executive – Anne studied physiology and neuroscience at university and carried out research into neuronal communication, before then following a career path with roles in academia and industry, and now as CE at the BNA. Anne will describe her own career in neuroscience, as well as some of the many other options open to you after a neuroscience degree.

The public has a fascination with the brain, but little attention is given to neuroscience education prior to graduate studies in brain-related fields. One reason may be the lack of low cost and engaging teaching materials. To address this, we have developed a suite of open-source tools which are appropriate for amateurs and for use in high school, undergraduate, and graduate level educational and research programs. This lecture will provide an overview of our mission to re-engineer research-grade lab equipment using first principles and will highlight basic principles of neuroscience in a "DIY" fashion: neurophysiology, functional electrical stimulation, micro-stimulation effect on animal behavior, neuropharmacology, even neuroprosthesis and optogenetics! Finally, with faculty academic positions becoming a scarce resource, I will discuss an alternative academic career path: entrepreneurship. It is possible to be an academic, do research, publish papers, present at conferences and train students all outside the traditional university setting. I will close by discussing my career path from graduate student to PI/CEO of a startup neuroscience company.