2 lecturer (assistant professor) in computer science (including Neurotechnology and/or AI) posts available at Ulster University https://www.jobs.ac.uk/job/DCF900/lecturer-in-computer-science https://www.ulster.ac.uk/about/jobs Salary: 7/8 (£39,369 - £56,054) Closing Date: 4th September 2023 at 23:59 Reference Number: 022187 The post holder will support the delivery of key areas of teaching and research in the discipline of Computing. They will contribute to the delivery of undergraduate and postgraduate degree programmes offered by the School of Computing, Engineering and Intelligent Systems, and undertake research in areas aligned with the Intelligent Systems Research Centre (ISRC). The School of Computing, Engineering & Intelligent Systems holds a Silver Athena SWAN Award in recognition of our commitment to advancing Gender equality. You can read more about what this means on our University website. The University has a range of initiatives to support a family friendly working environment, including flexible working. The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities. Appointment will be made on merit.

We are looking for an excellent candidate with a master’s degree in MSc in Artificial Intelligence, Computer Science, Mathematics, Statistics, or a closely related field to join a project focused on developing an advanced transparent machine learning framework with application on movement behavioural analysis. Smartwatches and other wearable technologies allow us to continuously collect data on our daily movement behaviour patterns. We would like to understand how machine learning techniques can be used to learn causal effects from time-series data to identify and recommend effective changes in daily activities (i.e., possible behavioural interventions) that are expected to result in concrete health improvements (e.g., improving cardiorespiratory fitness). This research, at the intersection of machine learning and causality, aims to develop algorithms for finding causal relations between behavioural indicators learned from the time series data and associated health-outcomes.

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.

Fordham University (New York City) has developed a unique Ph.D. program in Computer Science, tuned to the latest demands and opportunities of the field. Upon completion of the Ph.D. in Computer Science program, students will be able to demonstrate the fundamental, analytical, and computational knowledge and methodology needed to conduct original research and practical experiments in the foundations and theory of computer science, in software and systems, and in informatics and data analytics. They will also be able to apply computing and informatics methods and techniques to understand, analyze, and solve a variety of significant, real-world problems and issues in the cyber, physical, and social domains. Furthermore, they will be able to conduct original, high-quality, ethically informed, scientific research and publish in respected, peer-reviewed, journals and conferences. Lastly, they will be able to effectively instruct others in a variety of topics in Computer Science at the university level, addressing ethics, justice, diversity, and sustainability. This training and education means that graduates can pursue careers at the university level, but also research and leadership positions in industry and government and within the leading technology companies. A hallmark of the program is early involvement in research, within the first two years of the program. Students will have the opportunity to carry out research in machine learning and AI/robotics, big data analytics and informatics, data and information fusion, information and cyber security, and software engineering and systems.

The Faculty of Computer Science of HSE University invites applications for full-time, tenure-track positions of Assistant Professor in all areas of computer science including but not limited to artificial intelligence, machine learning, computer vision, programming language theory, software engineering, system programming, algorithms, computation complexity, distributed and parallel computation, bioinformatics, human-computer interaction, and robotics. The successful candidate is expected to conduct high-quality research publishable in reputable peer-reviewed journals with research support provided by the University.

The Machine Learning and Knowledge Discovery Group of INESC-ID is looking for qualified applicants for three fully funded PhD student positions on topics related with the application of deep learning techniques to problems with societal impact. These positions are funded by a large-scale research project in responsible AI, supported by the Resiliency and Recovery Facility. The successful candidates will pursue a PhD degree in Computer Science and Engineering at Instituto Superior Técnico, in Lisbon Portugal. The broad topics of research are: 1 - Normalization of geolocation records using deep learning techniques, 2 - High confidence information retrieval and question answering, 3 - Application of reinforcement learning methods to the generation of efficient algorithms.

The KINDI Center for Computing Research at the College of Engineering in Qatar University is seeking high-caliber candidates for a research faculty position at the level of assistant professor in the area of artificial intelligence (AI). The applicant should possess a Ph.D. degree in Computer Science or Computer Engineering or related fields from an internationally recognized university and should demonstrate an outstanding research record in AI and related subareas (e.g., machine/deep learning (ML/DL), computer vision, robotics, natural language processing, etc.) and fields (e.g., data science, big data analytics, etc.). Candidates with good hands-on experience are preferred. The position is available immediately.

2 lecturer (assistant professor) in computer science (including Neurotechnology & AI) posts available at Ulster University https://www.jobs.ac.uk/job/DCF900/lecturer-in-computer-science https://www.ulster.ac.uk/about/jobs Salary: 7/8 (£39,369 - £56,054) Closing Date: 4th September 2023 at 23:59 Reference Number: 022187 The post holder will support the delivery of key areas of teaching and research in the discipline of Computing. They will contribute to the delivery of undergraduate and postgraduate degree programmes offered by the School of Computing, Engineering and Intelligent Systems, and undertake research in areas aligned with the Intelligent Systems Research Centre (ISRC). The School of Computing, Engineering & Intelligent Systems holds a Silver Athena SWAN Award in recognition of our commitment to advancing Gender equality. You can read more about what this means on our University website. The University has a range of initiatives to support a family friendly working environment, including flexible working. The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities. Appointment will be made on merit.

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.

We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. However, the CTM is not a standard Turing Machine. It’s not the input-output map that gives the CTM its feeling of consciousness, but what’s under the hood. Nor is the CTM a standard GW model. In addition to its architecture, what gives the CTM its feeling of consciousness is its predictive dynamics (cycles of prediction, feedback and learning), its internal multi-modal language Brainish, and certain special Long Term Memory (LTM) processors, including its Inner Speech and Model of the World processors. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature. Reference. L. Blum and M. Blum, "A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine," PNAS, vol. 119, no. 21, 24 May 2022. https://www.pnas.org/doi/epdf/10.1073/pnas.2115934119

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

The vertebrate retina is an important outpost of the central nervous system, responsible for the perception and transmission of visual information. It consists of five different types of neurons that reproducibly laminate into three layers, a process of crucial importance for the organ’s function. Unsurprisingly, impaired fate decisions as well as impaired neuronal migrations and lamination lead to impaired retinal function. However, how processes are coordinated at the cellular and tissue level and how variable or robust retinal formation is, is currently still underexplored. In my lab, we aim to shed light on these questions from different angles, studying on the one hand differentiation phenomena and their variability and on the other hand the downstream migration and lamination phenomena. We use zebrafish as our main model system due to its excellent possibilities for live imaging and quantitative developmental biology. More recently we also started to use human retinal organoids as a comparative system. We further employ cross disciplinary approaches to address these issues combining work of cell and developmental biology, biomechanics, theory and computer science. Together, this allows us to integrate cell with tissue-wide phenomena and generate an appreciation of the reproducibility and variability of events.

Our world is full of living creatures that must share information to survive and reproduce. As humans, we easily forget how hard it is to communicate within natural environments. So how do organisms solve this challenge, using only natural resources? Ideas from computer science, physics and mathematics, such as energetic cost, compression, and detectability, define universal criteria that almost all communication systems must meet. We use insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen’s location.

The cortical algorithm hypothesis states that there is one common computational framework to solve diverse cognitive problems such as vision, voice recognition and motion control. In my talk, I propose a strategy to guide the search for this algorithm and I present a few ideas on how some of its components might look like. I'll explain why a highly interdisciplinary approach is needed from neuroscience, computer science, mathematics and physics to make further progress in this important question.

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand precisely how neural circuit computations mechanistically cause behavior. Establishing this type of causal understanding will require multiple perturbational (e.g optogenetic) experiments. It has been unclear exactly how many such experiments are needed and how this number scales with the size of the nervous system in question. Here, using techniques from Theoretical Computer Science, we prove that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in many cases, unless a widely-posited hypothesis about computation is false (i.e. unless P = NP). Furthermore, using data and estimates, we demonstrate that the feasible experimental regime is typically one where the number of experiments performable scales sub-linearly in the number of neurons in the nervous system. This remarkable gulf between the worst-case and the feasible suggests an algorithmic barrier to such an understanding. Determining which notions of understanding are algorithmically tractable to establish in what contexts, thus, becomes an important new direction for investigation. TL; DR: Non-existence of tractable algorithms for neural circuit interrogation could pose a barrier to comprehensively understanding how neural circuits cause behavior. Preprint: https://biorxiv.org/content/10.1101/639724v1/…

The Aging Brain Initiative is an ambitious interdisciplinary effort by MIT focusing on understanding neurodegeneration and efforts to find hallmarks of aging, both in health and disease. The Initiative is broad, made up of scientists in several areas, including systems neuroscience, cell biology, engineering and computational biology, with core investigators from the Departments of Biology, Brain & Cognitive Sciences, Biological Engineering, and Computer Science & Artificial Intelligence Labs. "The theme of this symposium is Cellular & Molecular Mechanisms of Neurodegeneration.