The Research Training Group 2853 “Neuroexplicit Models of Language, Vision, and Action” is looking for 3 PhD students and 1 postdoc. Neuroexplicit models combine neural and human-interpretable (“explicit”) models in order to overcome the limitations that each model class has separately. They include neurosymbolic models, which combine neural and symbolic models, but also e.g. combinations of neural and physics-based models. In the RTG, we will improve the state of the art in natural language processing (“Language”), computer vision (“Vision”), and planning and reinforcement learning (“Action”) through the use of neuroexplicit models and investigate the cross-cutting design principles of effective neuroexplicit models (“Foundations”).

The Initiative for Data & Artificial Intelligence at Universidad de Chile is looking for Postdoctoral Researchers to join a collaborative team of PIs working on theoretical and applied aspects of Data Science. The role of the postholder(s) is twofold: first, they will engage and collaborate in current projects at the Initiative related to statistical machine learning, natural language processing and deep learning, with applications to time series analysis, health informatics, and astroinformatics. Second, they are expected to bring novel research lines affine to those currently featured at the Initiative, possibly in the form of theoretical work or applications to real-world problems of general interest. These positions are offered on a fixed term basis for up to one year with a possibility for a further year extension.

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.

The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.

The Institute for Language, Cognition and Computation (ILCC) at the University of Edinburgh invites applications for three-year PhD studentships starting in September 2024. ILCC is dedicated to the pursuit of basic and applied research on computational approaches to language, communication and cognition. Primary research areas include: Natural language processing and computational linguistics, Machine Translation, Speech technology, Dialogue, multimodal interaction, language and vision, Computational Cognitive Science, including language and speech, decision-making, learning and generalization, Social Media and Computational Social Science, Human-Computer interaction, design informatics, assistive and educational technology, Information retrieval and visualization. Approximately 10 studentships from a variety of sources are available, covering both maintenance at the research council rate of GBP 19,162 (2024/25 rates) per year and tuition fees. Awards increase every year, typically with inflation. Studentships are available for UK, EU, and non-EU nationals.

The Department of Computer Science in the College of Arts and Sciences at American University invites applications for a full-time, open-rank, tenure-line position beginning August 1, 2024. Applicants should have a PhD or an anticipated PhD completion by August 2024 in Computer Science or related fields. Depending on experience and qualification, the appointee to this position may be recommended for tenure at the time of hiring. Candidates can apply at the assistant, associate, or full professor level and we welcome applications from both academic and nonacademic organizations. We are looking for candidates who are excited at the prospect of joining a growing department where they will be able to make their mark. Preference will be given to candidates with a record of high-quality scholarship. For candidates applying at the associate or full professor level, a record of external funding is also expected. The committee will consider candidates engaged in high-quality research in any area of Computer Science related to Artificial Intelligence (E.g., Natural Language Processing, Machine Learning, Network Analysis, Information Visualization), Theoretical Computer Science (Computational Theory, Graph Theory, Algorithms), Cybersecurity, and other traditional areas of Computer Science (E.g., Software Engineering, Database Systems, Graphics, etc.). The University has areas of strategic focus for research in Data Science and Analytics, Health, Security, Social Equity, and Sustainability. Applicants from historically underrepresented minority and identity groups are strongly encouraged to apply. In addition to scholarship and teaching, responsibilities will include participation in department, school, and university service activities. Attention to Diversity, Equity and Inclusion (DEI) in all activities within the academic environment are expected.

The position integrates into an attractive environment of existing activities in artificial intelligence such as machine learning for robotics and computer vision, natural language processing, recommender systems, schedulers, virtual and augmented reality, and digital forensics. The candidate should engage in research and teaching in the general area of artificial intelligence. Examples of possible foci include machine learning for pattern recognition, prediction and decision making, data-driven, adaptive, learning and self-optimizing systems, explainable and transparent AI, representation learning; generative models, neuro-symbolic AI, causality, distributed/decentralized learning, environmentally-friendly, sustainable, data-efficient, privacy-preserving AI, neuromorphic computing and hardware aspects, knowledge representations, reasoning, ontologies. Cooperations with research groups at the Department of Computer Science, the Research Areas and in particular the Digital Science Center of the University as well as with business, industry and international research institutions are expected. The candidate should reinforce or complement existing strengths of the Department of Computer Science.

The University of Wisconsin-Madison's Information School seeks highly qualified candidates for up to two tenured positions in information sciences. These faculty positions will be academic nine-month, tenure-track appointments at the Associate Professor level, to start August 2024. Applications at the Professor level may be considered in exceptional cases. Applications are specifically encouraged in, but not limited to, the following areas: Natural language processing and information retrieval, e.g., applied natural language processing, text analysis, text and multimedia retrieval, recommendation systems, conversational systems. Computational social sciences, e.g., analytics and modeling of political behavior; computational analysis of social networks; algorithms and social media analytics; social simulation of organizational behavior. Policy analysis or policy-making studies of information or data security/risk/assurance, privacy, data governance, or data management. ML/AI, computation, and the future of work. Computational and information technologies in relation to children and/or elderly populations.

The computer science department at the IT University of Copenhagen is recruiting a new faculty member to join the natural language processing group. Candidates from any background and all areas of natural language processing are very welcome to apply!

The Research Training Group 2853 “Neuroexplicit Models of Language, Vision, and Action” is looking for 6 PhD students and 1 Postdoc. Neuroexplicit models combine neural and human-interpretable (“explicit”) models in order to overcome the limitations that each model class has separately. They include neurosymbolic models, which combine neural and symbolic models, but also e.g. combinations of neural and physics-based models. In the RTG, we will improve the state of the art in natural language processing (“Language”), computer vision (“Vision”), and planning and reinforcement learning (“Action”) through the use of neuroexplicit models and investigate the cross-cutting design principles of effective neuroexplicit models (“Foundations”).

Neuroexplicit models combine neural and human-interpretable ('explicit') models to overcome the limitations that each model class has separately. They include neurosymbolic models, which combine neural and symbolic models, as well as combinations of neural and physics-based models. The Research Training Group (RTG) aims to improve the state of the art in natural language processing ('Language'), computer vision ('Vision'), and planning and reinforcement learning ('Action'), and to develop novel machine learning techniques for neuroexplicit models ('Foundations'). The goal is to contribute to a better understanding of the cross-cutting design principles of effective neuroexplicit models through interdisciplinary collaboration.

The College of Information Sciences and Technology at Penn State is seeking a postdoctoral scholar to join an interdisciplinary team focusing on Human-AI collaboration to train mental health workers. The project is supported by an NSF grant. The position will involve developing computational methods to assess clinical sessions and provide actionable feedback to support effective training. An ideal candidate will have strong research skills and experience in relevant areas (e.g., foundation models, deep learning, natural language processing, reinforcement learning). It will be a full-time appointment for 24 months, with a possibility of renewal dependent upon performance.

The College of Arts and Sciences and the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington invite applications for multiple open-rank, tenured or tenure-track faculty positions in one or more of the following areas: artificial intelligence, human intelligence, and machine learning to begin in Fall 2025 or after. Appointments will be in one or more departments, including Cognitive Science, Computer Science, Informatics, Intelligent Systems Engineering, Mathematics, and Psychological and Brain Sciences. We encourage applications from scholars who apply interdisciplinary perspectives across these fields to a variety of domains, including cognitive science, computational social sciences, computer vision, education, engineering, healthcare, mathematics, natural language processing, neuroscience, psychology, robotics, virtual reality, and beyond. Reflecting IU’s strong tradition of interdisciplinary research, we encourage diverse perspectives and innovative research that may intersect with or extend beyond these areas. The positions are part of a new university-wide initiative that aims to transform our understanding of human and artificial intelligence, involving multiple departments and schools, as well as the new Luddy Artificial Intelligence Center.

The University of Rochester’s Department of Computer Science seeks to hire an outstanding early-career candidate in the area of Artificial Intelligence. Specifically, we are looking to hire a tenure-track Assistant Professor in any of the following areas: Learning Theory, especially related to deep learning, Machine Learning Systems (ML Ops, memory efficient training techniques, distributed model training methods with GPUs/accelerators, etc.), or Deep reinforcement learning. We are especially interested in applications of these areas to large language models. Exceptional candidates at the associate or full professor level, or in other AI research areas such as foundational research in natural language processing (NLP), are also encouraged to apply. Candidates must have (or be about to receive) a doctorate in computer science or a related discipline.

The Natural Language Processing for Biomedical Information Analysis (NLP4BIA) group at BSC is an internationally renowned research group working on the development of NLP, language technology, and text mining solutions applied primarily to biomedical and clinical data. It is a highly interdisciplinary team, funded through competitive European and National projects requiring the implementation of natural language processing and advanced AI solutions making use of diverse technologies, including Transformers and recent advances in Large Language Models (LLM) to improve healthcare data analysis. The NLP4BIA-BSC is looking for a Research Engineer with experience in Language Technologies and Deep Learning. The candidate will be involved in technical work related to international projects, being part of a team of researchers working on topics related to clinical Language Models, multilingual NLP, benchmarking of language technology solutions and predictive content mining. The candidate will have the opportunity to advance the state of the art of biomedical language models and NLP methods working in a multidisciplinary environment alongside AI experts, computational linguists, clinical experts, and other engineers.

When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains. For this we need meta-research that is reproducible and updatable, or living meta-research. In this talk, we introduce the concept of living meta-research, examine prequels to this idea, and point towards standards and technologies that could assist researchers in doing living meta-research. We introduce technologies like natural language processing, which can help with automation of meta-research, which in turn will make the research easier to reproduce/update. Further, we showcase our open-source litmining ecosystem, which includes pubget (for downloading full-text journal articles), labelbuddy (for manually extracting information), and pubextract (for automatically extracting information). With these tools, you can simplify the tedious data collection and information extraction steps in meta-research, and then focus on analyzing the text. We will then describe some living meta-research projects to illustrate the use of these tools. For example, we’ll show how we used GPT along with our tools to extract information about study participants. Essentially, this talk will introduce you to the concept of meta-research, some tools for doing meta-research, and some examples. Particularly, we want you to take away the fact that there are many interesting open questions in meta-research, and you can easily learn the tools to answer them. Check out our tools at https://litmining.github.io/

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of complex narratives presented through auditory speech, written text, or visual images. To understand how complex language-related information is represented in the human brain there is a necessity to map the different linguistic and non-linguistic information perceived under different modalities across the cerebral cortex. To map this information to the brain, I suggest following a naturalistic approach and observing the human brain performing tasks in its naturalistic setting, designing quantitative models that transform real-world stimuli into specific hypothesis-related features, and building predictive models that can relate these features to brain responses. In my talk, I will present models of brain responses collected using functional magnetic resonance imaging while human participants listened to or read natural narrative stories. Using natural text and vector representations derived from natural language processing tools I will present how we can study language processing in the human brain across modalities, in different levels of temporal granularity, and across different languages.

In recent years, artificial neural networks have demonstrated human-like or super-human performance in many tasks including image or speech recognition, natural language processing (NLP), playing Go, chess, poker and video-games. One remarkable feature of the resulting models is that they can develop very intuitive latent representations of their inputs. In these latent spaces, simple linear operations tend to give meaningful results, as in the well-known analogy QUEEN-WOMAN+MAN=KING. We postulate that human brain representations share essential properties with these deep learning latent spaces. To verify this, we test whether artificial latent spaces can serve as a good model for decoding brain activity. We report improvements over state-of-the-art performance for reconstructing seen and imagined face images from fMRI brain activation patterns, using the latent space of a GAN (Generative Adversarial Network) model coupled with a Variational AutoEncoder (VAE). With another GAN model (BigBiGAN), we can decode and reconstruct natural scenes of any category from the corresponding brain activity. Our results suggest that deep learning can produce high-level representations approaching those found in the human brain. Finally, I will discuss whether these deep learning latent spaces could be relevant to the study of consciousness.

Climate change is one of our generation's greatest challenges, with increasingly severe consequences on global ecosystems and populations. Machine Learning has the potential to address many important challenges in climate change, from both mitigation (reducing its extent) and adaptation (preparing for unavoidable consequences) aspects. To present the extent of these opportunities, I will describe some of the projects that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, advancing the state-of-the-art in Machine Learning while ensuring that this fundamental progress translates into positive real-world impact.

In 1955, John McCarthy and colleagues proposed an AI summer research project with the following aim: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” More than six decades later, all of these research topics remain open and actively investigated in the AI community. While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and robotics, current AI systems still almost entirely lack the ability to form humanlike concepts and abstractions. Some cognitive scientists have proposed that analogy-making is a central mechanism for conceptual abstraction and understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on prospects for developing AI systems with humanlike abilities for abstraction and analogy.