The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation over four years. Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP. The studentships are fully funded for the four years and come with a generous allowance for travel, equipment and research costs. The CDT brings together researchers in NLP, speech, linguistics, cognitive science and design informatics from across the University of Edinburgh. Students will be supervised by a world-class faculty comprising almost 60 supervisors and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs. The CDT involves a number of industrial partners, including Amazon, Facebook, Huawei, Microsoft, Naver, Toshiba, and the BBC. Links also exist with the Alan Turing Institute and the Bayes Centre.

We are announcing one or more 2-year postdoc positions in identification and analysis of lexical semantic change using computational models applied to diachronic texts. Our languages change over time. As a consequence, words may look the same, but have different meanings at different points in time, a phenomenon called lexical semantic change (LSC). To facilitate interpretation, search, and analysis of old texts, we build computational methods for automatic detection and characterization of LSC from large amounts of text. Our outputs will be used by the lexicographic R&D unit that compiles the Swedish Academy dictionaries, as well as by researchers from the humanities and social sciences that include textual analysis as a central methodological component. The Change is Key! program and the Towards Computational Lexical Semantic Change Detection research project offer a vibrant research environment for this exciting and rapidly growing cutting-edge research field in NLP. There is a unique opportunity to contribute to the field of LSC, but also to humanities and social sciences through our active collaboration with international researchers in historical linguistics, analytical sociology, gender studies, conceptual history, and literary studies.

An exciting opportunity has opened up on my team at Amazon. We're seeking a Senior Applied Scientist to join the Selling Partner Communities (SPC) Science team and lead our multi-year voice of seller Machine Learning projects. This hands-on, pivotal role will take research to production, leveraging NLP, sentiment analysis, LLMs, RAGs, and causal modeling. The job location could be in Seattle, San Diego, or Arlington, in a hybrid work from home and office setting. In this position, you'll collaborate closely with senior leadership to identify high-impact opportunities based on diverse user feedback and develop novel ML approaches to address them. This includes designing and deploying sophisticated NLP models, building scalable data pipelines, and integrating cutting-edge ML-based solutions to directly support Amazon's vast network of selling partners. Key responsibilities: Partner cross-functionally to define requirements, set success metrics, and deliver impactful ML solutions Extract insights from advanced techniques like sentiment analysis, named entity recognition, and time series analysis to inform product enhancements Continuously research and evaluate new ML/NLP approaches to enhance current solutions Work closely with data/software engineers to seamlessly integrate successful models into production systems Publish at top-tier research conferences and mentor junior scientists, providing feedback on their work

The NLP4BIA group at the Barcelona Supercomputing Center (BSC) is looking for a post-doctoral researcher with expertise in data science, NLP and LMs. 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 Postdoctoral 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 multilingual information extraction in the clinical field, including Named-Entity Recognition, Entity Linking and Language Modeling. The candidate will have the opportunity to advance the state of the art of cross-lingual biomedical NLP methods by working in a multidisciplinary environment alongside linguists, medical experts, and other engineers. The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility - Next Generation, within the framework of the General Invitation by the public business entity Red.es to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.

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.

We (Sarath Chandar and Amal Zouaq) are seeking multiple postdoctoral researchers in Natural Language Processing (NLP) to work on large language models. The postdocs will be largely involved in various projects in LLMs, including but not limited to the following topics: Multi-agent / modular LLMs, LLM safety and Alignment, Bias and Fairness, Efficient Training Methods, Non-parametric memories, Constrained generation, LLMs and foundation models for biology and medical data. This position will be at Mila, the world-renowned AI hub located in Montreal, Canada – home to over 1000 researchers pushing the boundaries of AI research.

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.