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Post-doc Namkin & Loria-Sémagramme - Troyes-Nancy, France
by Maxime Amblard 18 Dec '23

18 Dec '23

The Namkin company and Loria - Université de Lorraine invites applications for a postdoctoral position on business event extraction. Location: Troyes, France and Nancy, France Application Deadline: 31st January 2024 Starting Date: March 2024 Contract Duration: 1 year (with possible extension) The industry faces numerous challenges that necessitate the evolution of BtoB marketing tools, in order to develop a valuable offer and provide an enhanced customer experience. Namkin's BrainLab develops industrial marketing tools for digitalizing customer relations, evolving business models, and exploiting business and economic data for business development. One of the key challenges of marketing intelligence is to identify risks and opportunities so as to guide marketing strategies. Among the sources of information useful to detect risks and opportunities, Namkin has identified Business Events, that is, “textually reported real-world occurrences, actions, relations, and situations involving companies and firms” (Jacobs et al., 2018). The Loria Semagram team specialises in modelling natural language semantics to represent discourse. While modern semantic representations may contain vast quantities of information, they do not always (or necessarily) contain the information that is useful for the concrete application. For instance, significant challenges still persist in dealing with temporal relations and finely-grained negation interpretation. A number of studies at the crossroads of business intelligence and NLP have focused on the detection or extraction of Business Events (e.g., Arendarenko & Kakkonen, 2012; Han et al., 2018; Jacobs et al., 2018; Jacobs & Hoste, 2020; Jacobs & Hoste, 2022). Despite the richness of the event extraction literature, many challenges still remain. Some of these challenges are concerned with the modelling of the task itself, such as the necessity / benefit of trigger identification for event extraction (see Zhu et al. 2021), some with the scope of the task, such as sentence level vs document level extraction (e.g., Zheng et al. 2019), some with the information necessary to the integration of events in a coherent knowledge base, like factuality detection (e.g., Zhang et al., 2022) and event disambiguation (e.g., Barhom et al., 2019). Recent research has looked into the benefits of exploiting semantic representations, and in particular Abstract Meaning Representation (AMR; Banarescu et al. 2013), for low-resources scenarios (Huang et al., 2018) and document level event argument extraction (e.g., Xu et al., 2022). However, it appears that AMR has to be adapted in order to optimally support event extraction related tasks (Yang et al., 2023). One major limitation of AMR for document-level event extraction is that AMR works at the sentence level, and thus requires the aggregation of sentence-level representations. AMR is also limited in terms of negation and universal quantification expressive power. To overcome these issues, we seek to appoint a Postdoctoral Researcher to work on semantic modelling. Some promising new lead was recently provided by Bos (2023) who proposes a new meaning representation system that overcomes expressive power limitations, supports discourse relations and inter-sentential coreferences, and reduces the annotation load. The appointed Postdoctoral Researcher will explore semantic modelling solutions and their application to event extraction in the field of business. The topic covers various subjects, including: - Computational semantics, - Machine learning with neural networks, - Cross-domain model transfer, - Learning from small data, - Combining top-down (expert-driven) and bottom-up (dataset-driven) models, - Design of meaning representations - Shallow and deep semantic processing and reasoning - Hybrid symbolic and statistical approaches to semantics - Neural semantic parsing - Semantics and ontologies The successful candidate will be part of Namkin's Data & IA team and the Sémagramme Team at Loria, with co-supervision provided by Agata Marcante and Professor Maxime Amblard. As part of the role, you will have the opportunity to... - Design, develop and test semantic representation algorithms for text-mining with the aim of identifying significant information in unstructured text. - Collaborate with Namkin’s experts to evaluate the algorithms on real-world use cases. You will be responsible for writing academic papers, technical reports and project deliverables. You will also attend academic conferences or project meetings to present your findings and act as a representative for the team. Requirements include expertise in semantic representation algorithms, excellent technical writing skills and the ability to work well in a team. * Applicants must hold a PhD in Computer Science, related to Data Systems, Natural Language Processing, or Artificial Intelligence. * They should have proven fluency in at least one programming language, such as Python, R, Java or C++. * Candidates must possess a curious and passionate attitude towards research and learning in general. * Proficiency in French language would be considered a bonus. * Previous experience in the NLP field would be considered advantageous. How to apply: send an email to: applications(a)namkin.fr <mailto:applications@namkin.fr> - with the subject starting with ''Namkin-Loria Postdoc'' - with a single PDF attached containing: * Cover letter detailing motivation and qualifications for this position. * Curriculum vitae, with a list of publications and contact details for references. Interested parties are encouraged to contact us for further information regarding the position before applying. References Arendarenko, E., & Kakkonen, T. (2012). Ontology-based information and event extraction for business intelligence. In Artificial Intelligence: Methodology, Systems, and Applications: 15th International Conference, AIMSA 2012, Varna, Bulgaria, September 12-15, 2012. Proceedings 15 (pp. 89-102). Springer Berlin Heidelberg. Barhom, S., Shwartz, V., Eirew, A., Bugert, M., Reimers, N., & Dagan, I. (2019). Revisiting joint modeling of cross-document entity and event coreference resolution. arXiv preprint arXiv:1906.01753. Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., ... & Schneider, N. (2013, August). Abstract meaning representation for sembanking. In Proceedings of the 7th linguistic annotation workshop and interoperability with discourse (pp. 178-186). Jacobs, G., & Hoste, V. (2020). Extracting fine-grained economic events from business news. In COLING 2020 (pp. 235-245). COLING. Jacobs, G., & Hoste, V. (2022). SENTiVENT: enabling supervised information extraction of company-specific events in economic and financial news. Language Resources and Evaluation, 56(1), 225-257. Jacobs, G., Lefever, E., & Hoste, V. (2018). Economic event detection in company-specific news text. In 1st Workshop on Economics and Natural Language Processing (ECONLP) at Meeting of the Association-for-Computational-Linguistics (ACL) (pp. 1-10). Association for Computational Linguistics (ACL). Han, S., Hao, X., & Huang, H. (2018). An event-extraction approach for business analysis from online Chinese news. Electronic Commerce Research and Applications, 28, 244-260. Huang, L., Ji, H., Cho, K., Dagan, I., Riedel, S., & Voss, C. (2018, July). Zero-Shot Transfer Learning for Event Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2160-2170). Xu, R., Wang, P., Liu, T., Zeng, S., Chang, B., & Sui, Z. (2022). A two-stream AMR-enhanced model for document-level event argument extraction. arXiv preprint arXiv:2205.00241. Yang, Y., Guo, Q., Hu, X., Zhang, Y., Qiu, X., & Zhang, Z. (2023). An AMR-based link prediction approach for document-level event argument extraction. arXiv preprint arXiv:2305.19162. Zhang, H., Qian, Z., Li, P., & Zhu, X. (2022, November). Evidence-Based Document-Level Event Factuality Identification. In PRICAI 2022: Trends in Artificial Intelligence: 19th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2022, Shanghai, China, November 10–13, 2022, Proceedings, Part II (pp. 240-254). Cham: Springer Nature Switzerland. Zheng, S., Cao, W., Xu, W., & Bian, J. (2019). Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. arXiv preprint arXiv:1904.07535. Zhu, T., Qu, X., Chen, W., Wang, Z., Huai, B., Yuan, N. J., & Zhang, M. (2021). Efficient document-level event extraction via pseudo-trigger-aware pruned complete graph. arXiv preprint arXiv:2112.06013. ---------------------- Maxime Amblard Université de Lorraine https://members.loria.fr/mamblard <https://members.loria.fr/mamblard> http://espoir-ul.fr <http://espoir-ul.fr/>

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Job offer: W1 Professorship for Digital Humanities
by Stefanie Dipper 18 Dec '23

18 Dec '23

Job offer: W1 Professorship for Digital Humanities in the Study of Religion (with Tenure Track W2) The Center for Religious Studies (CERES) at the Ruhr University Bochum (RUB), Germany, invites applications for the position of a W1 Professorship for Digital Humanities in the Study of Religion (with Tenure Track W2). The successful applicant is expected to represent the field of Digital Humanities in the study of religion in research and teaching. The aim is to strengthen Digital Humanities at RUB and the study of religion at CERES and especially in the context of the Collaborative Research Center "Metaphors of Religion". The full text of the advertisement (in German) is available at https://jobs.ruhr-uni-bochum.de/jobposting/8c4b276c0e75e597169c5082e2051ca7… (for an unofficial English version, see https://ceres.rub.de/en/news/w1-professur-fur-digital-humanities-in-der-rel…). Address for questions and applications: Dr. Tim Karis/tim.karis(a)rub.de Application deadline: 2024, Jan 31 Stefanie Dipper

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[CfP] DLnLD: Deep Learning and Linked Data
by Gilles Sérasset 18 Dec '23

18 Dec '23

DLnLD: Deep Learning and Linked Data Workshop colocated with LREC-COLING 2024, Date: May 21, 2024 Venue: Torino, Italy and online For up to date info, check: https://dl-n-ld.github.io/ <https://dl-n-ld.github.io/> Call for Papers ---------------------------------------------------------------------------------------- What does Linguistic Linked Data brings to Deep Learning and vice versa ? Let’s bring together these two complementary approaches in NLP. ---------------------------------------------------------------------------------------- Motivations for the Workshop Since the appearance of transformers (Vaswani et al., 2017), Deep Learning (DL) and neural approaches have brought a huge contribution to Natural Language Processing (NLP) either with highly specialized models for specific application or via Large Language Models (LLMs) (Devlin et al., 2019; Brown et al., 2020; Touvron et al., 2023) that are efficient few-shot learners for many NLP tasks. Such models usually build on huge web-scale data (raw multilingual corpora and annotated specialized, task related, corpora) that are now widely available on the Web. This approach has clearly shown many successes, but still suffers from several weaknesses, such as the cost/impact of training on raw data, biases, hallucinations, explainability, among others (Nah et al., 2023). The Linguistic Linked Open Data (LLOD) (Chiarcos et al., 2013) community aims at creating/distributing explicitly structured data (modelled as RDF graphs) and interlinking such data across languages. This collection of datasets, gathered inside the LLOD Cloud (Chiarcos et al., 2020), contains a huge amount of multilingual ontological (e.g. DBpedia (Lehmann et al., 2015)); lexical (e.g., DBnary (Sérasset, 2015), Wordnet (McCrae et al., 2014), Wikidata (Vrandečić and Krötzsch, 2014)); or linguistic (e.g., Universal Dependencies Treebank (Nivre et al., 2020; Chiarcos et al., 2021), DBpedia Abstract Corpus (Brümmer et al., 2016)) information, structured using common metadata (e.g., OntoLex (McCrae et al., 2017), NIF (Hellmann et al., 2013), etc.) and standardised data categories (e.g., lexinfo (Cimiano et al., 2011), OliA (Chiarcos and Sukhareva, 2015)). Both communities bring striking contributions that seem to be highly complementary. However, if knowledge (ontological) graphs are now routinely used in DL, there is still very few research studying the value of Linguistic/Lexical knowledge in the context of DL. We think that, today, there is a real opportunity to bring both communities together to take the best of both worlds. Indeed, with more and more work on Graph Neural Networks (Wu et al., 2023) and Embeddings on RDF graphs (Ristoski et al., 2019), there is more and more opportunity to apply DL techniques to build, interlink or enhance Linguistic Linked Open Datasets, to borrow data from the LLOD Cloud for enhancing Neural Models on NLP tasks, or to take the best of both worlds for specific NLP use cases. Submission Topics This workshop aims at gathering researchers that work on the interaction between DL and LLOD in order to discuss what each approach has to bring to the other. For this, we welcome contributions on original work involving some of the following (non exhaustive) topics: • Deep Learning for Linguistic Linked Data, among which (but not exclusively): • Modelling, Resources & Interlinking, • Relation Extraction • Corpus annotation • Ontology localization • Knowledge/Linguistic Graphs creation or expansion • Linguistic Linked Data for Deep Learning, among which (but not exclusively): • Linguistic/Knowledge Graphs as training data • Fine tuning LLMs using Linguistic Linked (meta)Data • Graph Neural Networks • Knowledge/Linguistic Graphs embeddings • LLOD for model explainability/sourcing • Neural models for under-resourced languages • Joint Deep Learning and Linguistic Data applications • Use cases combining Language Models and Structured Linguistic Data • LLOD and DL for Digital Humanities • Question-Answering on graph data All application domains (Digital Humanities, FinTech, Education, Linguistics, Cybersecurity…) as well as approaches (NLG, NLU, Data Extraction…) are welcome, provided that the work is based on the use of BOTH Deep Learning techniques and Linguistic Linked (meta)Data. Important Dates (Current dates are tentative and will be revised when we will have more input from LREC-COLING Workshop Chairs) All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”) • Final submissions due: 25 February 2024 • Notification of acceptance: 25 March 2024 • Camera-ready due: 2nd April 2024 Authors kit All papers must follow the LREC-COLING 2024 two-column format, using the supplied official style files. The templates can be downloaded from the Style Files and Formatting page provided on the website. Please do not modify these style files, nor should you use templates designed for other conferences. Submissions that do not conform to the required styles, including paper size, margin width, and font size restrictions, will be rejected without review. LREC-COLING 2024 Author’s Kit Page: https://lrec-coling-2024.org/authors-kit/ <https://lrec-coling-2024.org/authors-kit/> Paper submission Submission is electronic, using the Softconf START conference management system. For the submission link, refer to DLnLD website: https://dl-n-ld.github.io/ <https://dl-n-ld.github.io/> Workshop Chairs • Gilles Sérasset, Université Grenoble Alpes, France • Hugo Gonçalo Oliveira, University of Coimbra, Portugal • Giedre Valunaite Oleskeviciene, Mykolas Romeris University, Lithuania Program Committee • Mehwish Alam, Télécom Paris, Institut Polytechnique de Paris, France • Russa Biswas, Hasso Plattner Institute, Potsdam, Germany • Milana Bolatbek, Al-Farabi Kazakh National University, Kazakhstan • Michael Cochez, Vrije Universiteit Amsterdam, Netherlands • Milan Dojchinovski, Czech Technical University in Prague, Czech Republic • Basil Ell, University of Oslo, Norway • Robert Fuchs, University of Hamburg, Germany • Radovan Garabík, L’. Štúr Institute of Linguistics, Slovak Academy of Sciences, Slovakia • Daniela Gifu, Romanian Academy, Iasi branch & Alexandru Ioan Cuza University of Iasi, Romania • Katerina Gkirtzou, Athena Research Center, Maroussi, Greece • Jorge Gracia del Río, University of Zaragoza, Spain • Dagmar Gromann, University of Vienna, Austria • Dangis Gudelis, Mykolas Romeris University, Lithuania • Ilan Kernerman, Lexicala by K Dictionaries, Israel • Chaya Liebeskind, Jerusalem College of Technology, Israel • Marco C. Passarotti, Università Cattolica del Sacro Cuore, Milan, Italy • Heiko Paulheim, University of Mannheim, Germany • Alexandre Rademaker, IBM Research Brazil and EMAp/FGV, Brazil • Georg Rehm, DFKI GmbH, Berlin, Germany • Harald Sack, Karlsruhe Institute of Technology, Karlsruhe, Germany • Didier Schwab, Université Grenoble Alpes, France • Ranka Stanković, University of Belgrade, Serbia • Andon Tchechmedjiev, IMT Mines Alès, France • Dimitar Trajanov, Ss. Cyril and Methodius University – Skopje, Macedonia • Ciprian-Octavian Truică, POLITEHNICA Bucharest, Romania • Nicolas Turenne, Guangdong University of Foreign Studies, China • Slavko Žitnik, University of Ljubljana, Slovenia

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Deadline extension (Dec 22): MOOMIN @ EACL 2024 - the first workshop on Modular and Open Multilingual NLP
by tiedeman＠gmail.com 17 Dec '23

17 Dec '23

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First CfP: MOOMIN @ EACL 2024 - the first workshop on Modular and Open Multilingual NLP
by Tiedemann, Jörg 17 Dec '23

17 Dec '23

First Call for papers: MOOMIN (the first workshop on Modular and Open Multilingual NLP) collocated with EACL 2024, March 21 or 22, 2024 Website: https://moomin-workshop.github.io/ Submission website: https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/MOOMIN We invite submissions to the first edition of the MOOMIN workshop on Modular and Open Multilingual NLP, to be held at EACL 2024 on March 21 or 22, 2024. [Important Dates] * Workshop paper due: December 18, 2023 * Resubmission deadline (for pre-reviewed ARR & main conference submissions): January 17, 2024 * Notification of acceptance: January 20, 2024 * Camera-ready papers due: January 30 2024 * Workshop dates: March 21-22, 2024 [Introduction] NLP in the age of monolithic large language models starts to hit the limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. This allows researchers to design scalable, wide-coverage, efficient and reusable models. Multilingual NLP is today faced with a number of difficult challenges. Scaling a multilingual model to a high number of languages is prone to suffer from negative interference, also known as the curse of multilinguality, leading to degradation in per-language performance, while earlier approaches to improving model capacity have hit the ceiling in terms of hardware, data and training algorithms. At the same time, we as a community wish to foster the development of open components that can be shared, deployed and widely integrated within the broader research community without incurring computational costs that add to the overall carbon footprint of NLP engineering. Modularity is a practical solution to answer all of these challenges and more, as it offers a very promising set of tools towards increased multilinguality of larger foundation models, either during their pretraining or in a post-hoc post-pretraining manner. [Topics of Interest] With this in mind, the MOOMIN workshop invites contributions related but not limited to the following topics: * mixture of expert models and gated routing * modular pre-training of multilingual language and translation models * effective transfer with modular architectures such as adapters and hypernetworks * efficient parallelization and distribution of modular model training * modular frameworks and architecture implementations * massively multilingual models with large language coverage * subnet selection and pruning * modular distillation * modular extensions of existing NLP models systems, especially in low-resource settings and for low-resource languages * evaluation of modular systems in terms of performance, efficiency, and computational costs * platforms for distributing, sharing, and integrating NLP components [Submission Guidelines] Authors are invited to submit original and unpublished research papers in the following categories: * Full papers (up to 8 pages) for substantial contributions. * Short papers (up to 4 pages) for ongoing or preliminary work. All submissions must be in PDF format, submitted electronically via OpenReview (https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/MOOMIN) and should follow the EACL 2024 formatting guidelines (following the ARR CfP<https://aclrollingreview.org/cfp>: use the official ACL style templates, which are available here<https://github.com/acl-org/acl-style-files>). We also intend to invite papers accepted to Findings to reach out to the organizing committee of MOOMIN to present their papers at the workshop, if in line with the topics as described above. [Workshop Organizers] * Timothee Mickus, University of Helsinki * Jörg Tiedemann, University of Helsinki * Ahmet Üstün, Cohere For AI * Raúl Vázquez, University of Helsinki * Ivan Vulić, University of Cambridge & PolyAI [Program Committee] A list of program committee members will be available on the workshop website. [Contact] For inquiries, please contact moomin.nlp.workshop(a)gmail.com<mailto:moomin.nlp.workshop@gmail.com>

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Deadline extension (Dec 22): UncertaiNLP @ EACL 2024 - The First Workshop on Uncertainty-Aware NLP
by Tiedemann, Jörg 17 Dec '23

17 Dec '23

Due to several requests we decided to extend the submission deadline for our workshop. The new deadline is Friday, December 22, 2023 Third Call for papers: UncertaiNLP – First Workshop on Uncertainty-Aware NLP @ EACL 2024, March 21 or 22, 2024 Website: https://uncertainlp.github.io/ Submission website: https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UncertaiNLP We invite submissions to the first edition of the UncertaiNLP workshop on Uncertainty-Aware NLP, to be held at EACL 2024 on March 21 or 22, 2024. [Important Dates] * Paper submission deadline: December 18, 2023 December 22, 2023 * Resubmission of already pre-reviewed ARR papers: January 17, 2024 * Notification of acceptance: January 20, 2024 * Camera-ready papers due: January 30 2024 * Workshop dates: March 21-22, 2024 [Workshop Topic and Content] Human languages are inherently ambiguous and understanding language input is subject to interpretation and complex contextual dependencies. Nevertheless, the main body of research in NLP is still based on the assumption that ambiguities and other types of underspecification can and have to be resolved. This workshop will provide a platform for research that embraces variability in human language and aims to represent and evaluate the uncertainty that arises from it, and from modeling tools themselves. Confirmed Invited Speakers: * Kristin Lennox (Exponent) * Mohit Bansal (UNC Chapel Hill) UncertaiNLP welcomes submissions to topics related (but not limited) to: * Frameworks for uncertainty representation * Theoretical work on probability and its generalizations * Symbolic representations of uncertainty * Documenting sources of uncertainty * Theoretical underpinnings of linguistic sources of variation * Data collection (e.g., to document linguistic variability, multiple perspectives, etc.) * Modeling * Explicit representation of model uncertainty (e.g., parameter and/or hypothesis uncertainty, Bayesian NNs in NLU/NLG, verbalised uncertainty, feature density, external calibration modules) * Disentangled representation of different sources of uncertainty (e.g., hierarchical models, prompting) * Reducing uncertainty due to additional context (e.g., additional context, clarification questions, retrieval/API augmented models) * Learning (or parameter estimation) * Learning from single and/or multiple references * Gradient estimation in latent variable models * Probabilistic inference * Theoretical and applied work on approximate inference (e.g., variational inference, Langevin dynamics) * Unbiased and asymptotically unbiased sampling algorithms * Decision making * Utility-aware decoders and controllable generation * Selective prediction * Active learning * Evaluation * Statistical evaluation of language models * Calibration to interpretable notions of uncertainty (e.g., calibration error, conformal prediction) * Evaluation of epistemic uncertainty [Submission Guidelines] Authors are invited to submit by December 18, 2023 original and unpublished research papers in the following categories: * Full papers (up to 8 pages) for substantial contributions. * Short papers (up to 4 pages) for ongoing or preliminary work. All submissions must be in PDF format, submitted electronically via OpenReview (https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UncertaiNLP) and should follow the EACL 2024 formatting guidelines (following the ARR CfP<https://aclrollingreview.org/cfp>: use the official ACL style templates, which are available here<https://github.com/acl-org/acl-style-files>). We also invite authors of papers accepted to Findings to reach out to the organizing committee of UncertaiNLP to present their papers at the workshop, if in line with the topics described above. Resubmission of already pre-reviewed ARR papers will be possible and more information will be sent in the later calls. [Workshop Organizers] * Wilker Aziz, University of Amsterdam * Joris Baan, University of Amsterdam * Hande Celikkanat, University of Helsinki * Marie-Catherine de Marneffe, UCLouvain/FNRS * Barbara Plank, LMU Munich * Swabha Swayamdipta, USC * Jörg Tiedemann, University of Helsinki * Dennis Ulmer, ITU Copenhagen [Program Committee] A list of program committee members will be available on the workshop website. [Contact] For inquiries, please contact uncertainlp(a)googlegroups.com<mailto:uncertainlp@googlegroups.com>

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6 PhD students and 1 Postdoc: Neuroexplicit Models of Language, Vision, and Action - Saarbrücken, Germany
by Vera Demberg 16 Dec '23

16 Dec '23

** *The Research Training Group 2853 “Neuroexplicit Models of Language, Vision, and Action” is looking for* * 6 PhD students - September 2024 1 Postdoc - March 2024 or later 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 RTG is scheduled to grow to a total of 24 PhD students and one postdocby 2025; the first six PhD students started in late 2023. Through the inclusion of ~20 further PhD students and postdocs funded from other sources, it will be one of the largest research centers on neuroexplicit or neurosymbolic models in the world. The RTG brings together researchers at Saarland University, the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, the CISPA Helmholtz Center for Information Security, and the German Research Center for Artificial Intelligence (DFKI). All of these institutions are colocated on the same campus in Saarbrücken, Germany. The positions are funded as follows: * PhD students will be funded for up to four years at the TV-L E13 100% pay scale. You should have or be about to complete an MSc degree in computer science or a related field and have demonstrated expertise in one of the research areas of the RTG, e.g. through an excellent Master’s thesis or relevant publications. * The postdoc will initially be funded for three years, with the possibility of extension up to five years, at the TV-L E13 100% pay scale.As the RTG postdoc, you will pursue your own research agenda in the field of neuroexplicit models and work with the PhD students to identify and pursue opportunities for collaborative research. You should have or be about to complete a PhD in computer science or a related field and have demonstrated your expertise in one or more of the RTG’s research areas through publications in top venues. The RTG is part of the Saarland Informatics Campus, one of the leading centers for researchin computer science, artificial intelligence, and natural language processing in Europe. The Saarland Informatics Campus brings together 900 researchers and 2500 students from 81 countries. The CISPA Helmholtz Center, located on the same campus, is home to an additional 350 researchers and on track to grow to 800 by 2026. Researchers at SIC and CISPA are part of the ELLIS network and have been awarded more than 35 ERC grants. Each PhD student in the RTG will be jointly supervised by two PhD advisorsfrom the list of Principal Investigators below. Each student will freely define their own research topic; we encourage the choice of topics that cross the traditional boundaries of research fields. Students may be affiliated with Saarland University or with one of the participating institutes. Vera Demberg, Saarland University - Computational Linguistics Jörg Hoffmann, Saarland University - AI Planning Eddy Ilg, Saarland University - Computer Vision, Machine Learning Dietrich Klakow, Saarland University - Natural Language Processing Alexander Koller, Saarland University - Computational Linguistics Bernt Schiele, MPI for Informatics - Computer Vision, Machine Learning Philipp Slusallek, DFKI and Saarland University - Computer Graphics, Artificial Intelligence Christian Theobalt, MPI for Informatics - Visual Computing, Machine Learning Mariya Toneva, MPI for Software Systems - Computational Neuroscience, Machine Learning Isabel Valera, Saarland University - Machine Learning Jilles Vreeken, CISPA - Machine Learning, Causality Joachim Weickert, Saarland University - Mathematical Data Analysis Verena Wolf, DFKI and Saarland University - Modeling and Simulation, Reinforcement Learning Ellie Pavlick, Brown University and Google AI, will join us regularly as a Mercator Fellow. Please send your application by 7 January 2024to apply(a)neuroexplicit.org <mailto:apply@neuroexplicit.org>. Include the reference number W2298 for the postdoc position and the reference number W2299 for the PhD positions. We aim to conduct job interviews in July (for a start in October) and September (for a later start). The legally binding version of this job ad is at https://www.uni-saarland.de/fileadmin/upload/verwaltung/stellen/Wissenschaf… <https://www.uni-saarland.de/fileadmin/upload/verwaltung/stellen/Wissenschaf…> (postdoc) and https://www.uni-saarland.de/fileadmin/upload/verwaltung/stellen/Wissenschaf… <https://www.uni-saarland.de/fileadmin/upload/verwaltung/stellen/Wissenschaf…> (PhD), respectively. For details on what materials to submit with your application and all other information about the RTG, please see our website: https://www.neuroexplicit.org/jobs/ <https://www.neuroexplicit.org/jobs/#phd-2023> *

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Re: Call for Papers- Special Session in IEEE-IJCNN under WCCI 2024 at Yokohama, Japan
by Prashant Gupta 16 Dec '23

16 Dec '23

On Sat, Dec 16, 2023 at 6:57 PM Prashant Gupta <guptaprashant1986(a)gmail.com> wrote: > Dear all, > Lots of greetings for the day. > > We (Dr. Bireshwar Dass Mazumdar and Dr. Prashant K. Gupta from Bennett > University, India) are organizing a “Special Session on Intelligent > Cognitive Computing (ICC) and Its Impact on Modern AI” at IEEE > International Joint Conference on Neural Networks (WCCI IEEE-IJCNN 2024) > which is going to be held as apart of IEEE World Congress on Computational > Intelligence 2024, Yokohama, Japan from June 30-July 5, 2024. > > IEEE WCCI 2024 (https://2024.ieeewcci.org/) is the world’s largest > technical event on computational intelligence, featuring the three flagship > conferences of the IEEE Computational Intelligence Society (CIS) under one > roof: The International Joint Conference on Neural Networks (IJCNN), the > IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and the IEEE > Congress on Evolutionary Computation (IEEE CEC). > > I request you to kindly circulate this mail in your organization and > encourage students, research scholars, and faculty members to submit their > research work at the respective venue. > > Information about the Special Session and the call of papers is available > on the link: https://sites.google.com/view/ssicc2024/home > > > > -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- > > Special Session on Intelligent Cognitive Computing (ICC) and Its Impact on > Modern AI > > Session Chairs: > > > - Dr. Bireshwar Dass Mazumdar, Associate Professor at School of > Computer Science Engineering & Technology, Bennett University, Greater > Noida, India. Email: bireshwardm(a)gmail.com > - Dr. Prashant K Gupta, Associate Professor at School of Computer > Science Engineering & Technology, Bennett University, Greater Noida, India. > Email: guptaprashant1986(a)gmail.com > > > > Important Dates: > > - Full paper submission: January 15, 2024 > - Notification of paper acceptance: March 15, 2024 > - Final Paper Submission & Early Registration Deadline: May 01, 2024 > - Conference: June 30- July 05, 2024 > > > We look forward to receiving your high-quality submissions. Please kindly > circulate this email among your contacts. > > -- > > *Dr. Prashant Gupta * > > *Associate Professor* > > > *School of Computer Science Engineering & Technology, * > > *Bennett University, Times of India Group, Greater Noida,* > > *Web: www.bennett.edu.in <http://www.bennett.edu.in>* > > > -- *Dr. Prashant Gupta * *Associate Professor* *School of Computer Science Engineering & Technology, * *Bennett University, Times of India Group, Greater Noida,* *Web: www.bennett.edu.in <http://www.bennett.edu.in>*

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Fwd: Call for Papers- Special Session in FUZZ-IEEE under WCCI 2024 at Yokohama, Japan
by Prashant Gupta 16 Dec '23

16 Dec '23

---------- Forwarded message --------- From: Prashant Gupta <guptaprashant1986(a)gmail.com> Date: Sat, Dec 16, 2023 at 6:51 PM Subject: Call for Papers- Special Session in FUZZ-IEEE under WCCI 2024 at Yokohama, Japan To: <corpora(a)lists.uib.no> Dear all, Lots of greetings for the day. I, Dr. Prashant K. Gupta, Bennett University, India, am organizing a “Special Session on Computing with Words (CWW): Emerging Topics & Applications” at IEEE International Conference on Fuzzy Systems (WCCI FUZZ-IEEE 2024) which is going to be held as apart of IEEE World Congress on Computational Intelligence 2024, Yokohama, Japan from June 30-July 5, 2024. IEEE WCCI 2024 (https://2024.ieeewcci.org/) is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The International Joint Conference on Neural Networks (IJCNN), the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and the IEEE Congress on Evolutionary Computation (IEEE CEC). I request you to kindly circulate this mail in your organization and encourage students, research scholars, and faculty members to submit their research work at the respective venue. Information about the Special Session and the call of papers is available on the link: https://sites.google.com/view/sscww2024/home -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Special Session on Computing with Words (CWW): Emerging Topics & Applications Session Chairs: Dr. Prashant K Gupta, Associate Professor at School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India. Email: guptaprashant1986(a)gmail.com Important Dates: - Full paper submission: January 15, 2024 - Notification of paper acceptance: March 15, 2024 - Final Paper Submission & Early Registration Deadline: May 01, 2024 - Conference: June 30- July 05, 2024 We look forward to receiving your high-quality submissions. Please kindly circulate this email among your contacts. -- *Dr. Prashant Gupta * *Associate Professor* *School of Computer Science Engineering & Technology, * *Bennett University, Times of India Group, Greater Noida,* *Web: www.bennett.edu.in <http://www.bennett.edu.in>* -- *Dr. Prashant Gupta * *Associate Professor* *School of Computer Science Engineering & Technology, * *Bennett University, Times of India Group, Greater Noida,* *Web: www.bennett.edu.in <http://www.bennett.edu.in>*

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Tenure Stream Assistant Professor Positions: Dalhousie University
by Hassan Sajjad 16 Dec '23

16 Dec '23

Full Ad: https://dal.peopleadmin.ca/postings/14872 The Faculty of Computer Science at Dalhousie University ( https://www.cs.dal.ca) invites applications for up to three tenure-stream Assistant Professor positions in any area of Computer Science. We are seeking candidates whose research focuses on one of our five *research clusters* <https://www.dal.ca/faculty/computerscience/research-industry/fcs_research.h…>: 1) Big Data Analytics, Artificial Intelligence & Machine Learning, 2) Human Computer Interaction, Visualization & Graphics, 3) Systems, 4) Algorithms & Bioinformatics and 5) Computer Science Education. Research areas of particular interest include but are not limited to: Computer Vision and Signal Understanding, Qualitative and Design Research in HCI, Natural Language Processing and Artificial Intelligence and Machine Learning. *Application Instructions: *Applications must include a cover letter, curriculum vitae, statements of research and teaching interests, sample publications, and the names and full contact information of three referees. Applications are due by *February 15, 2024*. All application materials should be submitted directly at *https://dal.peopleadmin.ca/postings/14872* <https://dal.peopleadmin.ca/postings/14872>. -- Regards; Hassan Sajjad

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