- Corpora - ELRA lists

Call for Participation: SemEval-2023 Shared Task 3 - Genre, Framing, and Persuasion Techniques Detection in Online News in a Multi-lingual Setup
by joedsm＠gmail.com 26 Sep '22

26 Sep '22

We are glad to invite you to participate in the SemEval-2023 Shared Task 3 on detecting the genre, the framing, and the persuasion techniques in online news. The main drive behind this task is to foster development of methods and tools to support the analysis of online media content in order to understand what makes a text persuasive: which writing style is used, what key aspects are highlighted, and which persuasion techniques are used to influence the reader. The data used for for this task is made of articles collected from 2020 to mid 2022, they revolve around a range of widely discussed topics such as COVID-19, climate change, abortion, migration, the Russo-Ukrainian war, and local elections. The data presents several novelties: it is multilabel, multilingual, uses an updated taxonomy of persuassion techniques and covers complementary dimensions of what makes a text persuasive. Are you interested in using AI systems to analyse political speech, media bias or rhetorics? Then you should not miss this task!!! URL https://propaganda.math.unipd.it/semeval2023task3/ TASKS We offer three subtasks on news articles in six languages (English, French, German, Italian, Polish, and Russian). Subtask 1: NEWS GENRE CATEGORISATION Given a news article, determine whether it is an opinion piece, aims at objective news reporting, or is a satire piece. This is a multi-class task at article-level. Subtask 2: NEWS FRAME CATEGORISATION Given a news article, identify the generic frames used in the article. This is a multi-class task at article-level. Subtask 3: PERSUASION TECHNIQUE DETECTION Given a news article, identify the persuasion techniques in each paragraph. This is a multi-label task at paragraph level. PARTICIPATION & EVALUATION The participants may take part in any number of subtask-language pairs (even just one), and may train their systems using the data for all languages (in a multilingual setup). To promote the development of language-agnostic solutions, there will be also two "surprise" languages for which we will release only test data for evaluation purposes. IMPORTANT DATES 23 September 2022: Registration opens 23 September 2022: Release of the first batch of the training\development set (next batches will follow regularly). 12 January 2023: Release of the test set 22 January 2023: Test submission site closes February 2023: Paper Submission Deadline March 2023: Notification to authors April 2023: Camera ready papers due Summer 2023: SemEval 2023 workshop TASK ORGANIZERS Giovanni Da San Martino, Preslav Nakov, Jakub Piskorski, Nicolas Stefanovitch

1 0

Postdoctoral position at Cardiff University
by Steven Schockaert 26 Sep '22

26 Sep '22

Location: Cardiff, UK Deadline for applications: 17th October 2022 Start date: 1st January 2023 (or later) Duration: 30 months Keywords: learning & reasoning, natural language processing, commonsense reasoning Details about the post Applications are invited for a Research Associate post in the Cardiff University School of Computer Science & Informatics, to work on the EPSRC Open Fellowship project ReStoRe (Reasoning about Structured Story Representations), which is focused on story-level language understanding. The overall aim of this project is to develop methods for learning graph-structured representations of stories. For this post, the specific focus will be on developing common sense reasoning strategies, based on graph neural networks, to fill the gap between what is explicitly stated in a story and what a human reader would infer by “reading between the lines”. More details about the post and instructions on how to apply are available here: https://krb-sjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partner… Background about the ReStoRe project When we read a story as a human, we build up a mental model of what is described. Such mental models are crucial for reading comprehension. They allow us to relate the story to our earlier experiences, to make inferences that require combining information from different sentences, and to interpret ambiguous sentences correctly. Crucially, mental models capture more information than what is literally mentioned in the story. They are representations of the situations that are described, rather than the text itself, and they are constructed by combining the story text with our commonsense understanding of how the world works. The field of Natural Language Processing (NLP) has made rapid progress in the last few years, but the focus has largely been on sentence-level representations. Stories, such as news articles, social media posts or medical case reports, are essentially modelled as collections of sentences. As a result, current systems struggle with the ambiguity of language, since the correct interpretation of a word or sentence can often only be inferred by taking its broader story context into account. They are also severely limited in their ability to solve problems where information from different sentences needs to be combined. As a final example, current systems struggle to identify correspondences between related stories (e.g. different news articles about the same event), especially if they are written from a different perspective. To address these fundamental challenges, we need a method to learn story-level representations that can act as an analogue to mental models. Intuitively, there are two steps involved in learning such story representations: first we need to model what is literally mentioned in the story, and then we need some form of commonsense reasoning to fill in the gaps. In practice, however, these two steps are closely interrelated: interpreting what is mentioned in the story requires a model of the story context, but constructing this model requires an interpretation of what is mentioned. The solution that is proposed in this fellowship is based on representations called story graphs. These story graphs encode the events that occur, the entities involved, and the relationships that hold between these entities and events. A story can then be viewed as an incomplete specification of a story graph, similar to how a symbolic knowledge base corresponds to an incomplete specification of a possible world. The proposed framework will allow us to reason about textual information in a principled way. It will lead to significant improvements in NLP tasks where a commonsense understanding is required of the situations that are described, or where information from multiple sentences or documents needs to be combined. It will furthermore enable a step change in applications that directly rely on structured text representations, such as situational understanding, information retrieval systems for the legal, medical and news domains, and tools for inferring business insights from news stories and social media feeds.

1 0

CFP - SIGIR Forum - December 2022 Edition
by Tirthankar Ghosal 26 Sep '22

26 Sep '22

CFP - SIGIR Forum - December 2022 Edition ===================================== Dear Colleague We invite you to submit your contribution to the upcoming December 2022 Edition of the SIGIR Forum, the official newsletter of the ACM Special Interest Group on Information Retrieval (SIGIR). The SIGIR Forum consists of two issues (June, December). It serves as a medium for disseminating general information and opinions on matters of interest to the IR community, conference and workshop reports, papers and book reviews, and Ph.D. dissertation abstracts. *** Call for Contributions for the December 2022 issue *** We invite contributions to the following categories, including: - Reports of IR-related conferences and workshops: Reports from the chairpersons of IR-related workshops (such as the satellite workshops of SIGIR, JCDL, or CIKM, or other workshops such as NTCIR, INEX) or IR-related conferences other than SIGIR (such as ECIR, HLT, CHIIR, SPIRE, or TREC); - Papers from IR-related invited talks which are not published in full in the relevant conference proceedings; - Papers describing new public infrastructures for IR research, such as in-depth descriptions of newly available test collections, newly available open-source or public domain IR software of particular relevance, new evaluation campaigns, etc.; - Papers about funding initiatives, industry trends, connections between research and industry, legal issues that are of potential interest to the IR community at large; - Any paper that, while of general interest to the IR community, is non-technical, and because of this would be unsuitable for publication in technical publishing forums such as the SIGIR Annual Conference; - Book reviews, bibliographies of general interest to the IR community; - Abstracts of recently published Ph.D. theses of interest to the general IR community. Note: Unless specifically stated, contents of the SIGIR Forum do not represent the official position of SIGIR or ACM. Contributions to the Forum are unrefereed papers unless otherwise indicated. The editorial board may desk-reject papers if they are out of scope. From June 2020 onwards, the SIGIR Forum newsletter is continuing only online. *** Important dates for the December 2022 Edition *** - 11 November 2022: Deadline for contributions - December 2022: Online publication *** Submission Instructions *** Kindly see http://sigir.org/forum/ for details on previous issues, template, and submission instructions and checklist. For inquiries about contributions, please contact the editors at editors_SIGIR(a)acm.org Tirthankar Ghosal (UFAL, Charles University, Prague) Josiane Mothe (IRIT, Univ. de Toulouse) Julián Urbano (Delft University of Technology) -- +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Tirthankar Ghosal Researcher at UFAL, Charles University, CZ https://member.acm.org/~tghosal +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

1 0

Job announcement: postdoc in the NLP group at the University of Mannheim
by Simone Paolo Ponzetto 26 Sep '22

26 Sep '22

Dear all, I am looking for a postdoctoral researcher to join us in the NLP group at the University of Mannheim [1,2]. Duties include teaching in our BSc and MSc Business Informatics and Data Science programs and contributing to the research activities of the group. Candidates should have a PhD in CL/NLP, computer science or a related discipline, a proven academic track record (as demonstrated by publications at top-tier conferences) and be good team players. Due to the position involving bachelor-level teaching, being fluent in German is definitely a plus, although not a must-have. We are located in Mannheim, a very multicultural city in the south-west of Germany. We offer a sustainable life/work balance and non-toxic academic environment. We are strongly committed to diversity and welcome applications from members of underrepresented groups. The position is available immediately and will remain open until filled (applications are considered as soon as they are submitted). The initial contract will be for 3 years with the option of extension. Salary range is according to the German public sector pay scale E13. Applications can be made per e-mail (jobs(a)informatik.uni-mannheim.de) and should include CV and publication record, names of two references and a research statement. All documents should be e-mailed as a single PDF with your last name as filename. Feel free to contact me if you have questions about the position! Best - Simone [1] https://tinyurl.com/r3j377nn [2] https://dblp.uni-trier.de/pid/04/2532.html -- Simone Paolo Ponzetto Data and Web Science Group University of Mannheim, Germany http://dws.informatik.uni-mannheim.de/ponzetto Tel: +49 621 181 2647

1 0

EVALITA 2023: Call for tasks - *** NEW DEADLINES and TIMELINE ***
by Giulia Venturi 26 Sep '22

26 Sep '22

EVALITA 2023: Call for tasks - NEW DEADLINES and TIMELINE EVALITA 2023 is an initiative of AILC (Associazione Italiana di Linguistica Computazionale, AILC https://www.ai-lc.it/). The final workshop will be held on **September 7th-8th in Parma**. As in the previous editions (https://www.evalita.it/), EVALITA 2023 will be organized along a few selected tasks, which provide participants with opportunities to discuss and explore both emerging and traditional areas of Natural Language Processing and Speech for Italian. The participation is encouraged for teams working both in academic institutions and industrial organizations. TASK PROPOSAL SUBMISSION Tasks proposals should be no longer than 4 pages and should include: - task title and acronym; - names and affiliation of the organizers (minimum 2 organizers); - brief task description, including motivations and state of the art; - explanation of the international relevance of the task; - description and examples of the data, including information about their availability, development stage, and issues concerning privacy and data sensitivity. The examples are mandatory because they are intended to give potential participants an idea of what the task data will look like, how it’ll be formatted, etc. - expected number of participants and attendees; - names and contact information of the organizers. In submitting your proposal, please bear in mind that we encourage: - challenging tasks involving linguistic analysis, e.g., beyond “simple” classification problems; - tasks focused on multimodality, e.g., considering both textual and visual information; - tasks characterized by different levels of complexity, e.g., with a straightforward main subtask and one or more sophisticated additional subtasks; - the re-annotation/expansion of datasets from previous years with new annotation levels, and texts from publicly available corpora; - both new tasks and re-runs: for new tasks, organizers will have to specify in the proposal why it would attract a reasonable number of participants, and why it is needed; - application-oriented tasks, that is tasks that have a clearly defined end-user application showcasing; - multilingual tasks, i.e. with data both in Italian and in other languages; - industrial tasks, i.e. tasks with real data provided by companies. The organizers of the accepted tasks should take care of planning, according to the scheduled deadlines (see below): - the development and distribution of datasets needed for the contest, i.e. data for training and development, and data for testing; the scorer to be used to evaluate the submitted systems should be included in the release of development data; - the development of task guidelines, where all the instructions for the participation are made clear together with a detailed description of data and evaluation metrics applied for the evaluation of the participant results; - the collection of participants results; - the evaluation of participants results according to standard metrics and baseline(s); - the solicitation of participation and of submissions; - the reviewing process of the papers describing the participants approach and results (according to the template to be made available by the EVALITA 2023 chairs); - the production of a paper describing the task (according to the template to be made available by the EVALITA 2023 chairs). *** Email your proposal in PDF format to evalita2023(a)gmail.com with "EVALITA 2023 TASK Proposal" as the subject line by the submission deadline: October 17th 2022. *** Please feel free to contact the EVALITA 2023 chairs at evalita2023(a)gmail.com in case of any questions or suggestions. NEW deadlines of the task proposal: - October 4th 2022 October 17th 2022: submission of task proposals - October 18th 2022 October 24th 2022: notification of task proposal acceptance NEW timeline of EVALITA 2023: - 7th February 2023: development data available to participants - 30th April 2023: registration closes - 2nd-19th May 2023: evaluation windows - 30th May 2023: assessment returned to participants - 14th June 2023: final reports (from participants) due to task organizers - 28th June 2023: final reports (from task organizers) due to EVALITA chairs - 10th July 2023: review deadline - 25th July 2023: camera ready version deadline - 7th-8th September 2023: final workshop in Parma EVALITA 2023 CHAIRS Mirko Lai (Università di Torino) Stefano Menini (Fondazione Bruno Kessler) Marco Polignano (Università di Bari Aldo Moro) Valentina Russo (Logogramma SRL) Rachele Sprugnoli (Università degli Studi di Parma) Giulia Venturi (Istituto di Linguistica Computazionale “A. Zampolli” - CNR)

1 0

Second call for the 2022 ALTA shared task
by Diego Molla-Aliod 26 Sep '22

26 Sep '22

CALL FOR PARTICIPATION http://www.alta.asn.au/events/sharedtask2022 The Australasian Language Technology Association (ALTA) is organising a programming competition for university undergraduate and postgraduate students. Following on the series of shared tasks by ALTA since 2010, all participants compete to solve the same problem. The problem highlights an active area of research and programming in the area of language technology. This year's shared task is a re-visit of the 2012 task: PIBOSO Sentence Classification - 10 years later. The goal of this task is to build automatic sentence classifiers that can map the content of biomedical abstracts into a set of pre-defined categories, which are used for Evidence-Based Medicine (EBM). EBM practitioners rely on specific criteria when judging whether a scientific article is relevant to a given question. They generally follow the PICO criterion: Population (P) (i.e., participants in a study); Intervention (I); Comparison (C) (if appropriate); and Outcome (O) (of an Intervention). Variations and extensions of this classification have been proposed, and for this task we will extend PICO by adding the classes Background (B) and Study Design (S); and including sentences that have no relevant content: Other (O). Therefore, the goal will be to classify the provided sentences according to the PIBOSO schema. Such information could be leveraged in various ways: e.g., to improve search performance; to enable structured querying with specific categories; and to aid users in more quickly making judgements against specified PICOSO criteria. The tentative key dates are: - Right Now - Registration and release of training and development data - 04 Oct 2022 - Release of test data - 11 Oct 2022 - Deadline of submission of runs - 14 Oct 2022 - Notification of results - 10 Nov 2022 - Deadline of submission of system description - 15-16 Dec 2022 - Presentation of results at ALTA 2022 Details of the task and registration are available at the competition website (http://www.alta.asn.au/events/sharedtask2022). Good luck! Diego Molla-Aliod -- Dr. Diego Mollá-Aliod Senior Lecturer School of Computing | Room 358 (Level 3), 4 Research Park Drive Macquarie University, NSW 2109, Australia T: +61 2 9850 9531 | F: +61 2 9850 9551 https://macquarie.zoom.us/my/diego.molla http://comp.mq.edu.au/~diego I acknowledge the traditional custodians of the land on which Macquarie University stands – the Wallumattagal clan of the Dharug nation – whose cultures and customs have nurtured and continue to nurture this land since time immemorial. I pay my respects to Elders past and present.

1 0

Assistant Professor, Teaching Stream - Computational Linguistics
by Ewan Dunbar 24 Sep '22

24 Sep '22

Assistant Professor, Teaching Stream - Computational Linguistics Closing Date: 11/14/2022, 11:59PM ET Description: The Department of Language Studies<https://www.utm.utoronto.ca/language-studies/> and the Department of Mathematical and Computational Sciences<https://www.utm.utoronto.ca/math-cs-stats/home> at the University of Toronto Mississauga invites applications for a full-time teaching stream appointment in the area of Computational Linguistics. The successful candidate will hold a joint appointment in the Department of Language Studies (55%) and in the Department of Mathematical and Computational Sciences (45%). The appointment will be at the rank of Assistant Professor, Teaching Stream with an expected start date of July 1, 2023, or shortly thereafter. The successful candidate must have earned a PhD in Linguistics, Computer Science, or Computer Science Education by the time of appointment or shortly thereafter, with a demonstrated record of excellence in teaching. The ideal candidate will have formal training in both linguistics and computer science. Candidates must have evidence of excellence in teaching in a degree granting program at the undergraduate program level, including (but not limited to) preparation and delivery of lessons, curriculum development, development of online material/lectures, and development of equity, diversity inclusion, and decolonization (EDID) initiatives. Additionally, candidates must possess a demonstrated commitment to excellent pedagogical practices and a demonstrated interest in teaching-related scholarly activities. Evidence of excellence in teaching and a commitment to excellent pedagogical inquiry can be demonstrated through teaching accomplishments, awards and accolades, publications and/or presentations at significant conferences, the teaching dossier submitted as part of the application including a strong teaching statement, pedagogically informed syllabi and course materials, outstanding teaching evaluations, as well as strong letters of reference from referees of high standing. Responsibilities include teaching introductory computer science and computational linguistics to students with diverse disciplinary backgrounds (esp. humanities and social sciences) in addition to linguistics and computational linguistics courses at intermediate and advanced undergraduate levels. The successful candidate will also be expected to create experiential learning opportunities for undergraduate students and to participate in the development of interdisciplinary programs that would involve linguistics and computer science. Scholarly activities for this position include active engagement in the scholarship of teaching and learning and/or disciplinary research as it relates to the faculty member’s teaching. In addition to teaching and scholarly activity, the successful candidate will be expected to perform standard professional and administrative activities typical of an academic department. The University of Toronto is an international leader in both Linguistic and Computer Science research and education. We seek candidates who demonstrate the intellectual curiosity and drive to pursue innovative pedagogical methods and pioneer new programs and curricula in Computational Linguistics. The successful candidate will join a vibrant group of teaching stream faculty members in both departments who are engaged in pedagogical and curricular innovations and research. Faculty are expected to combine their expertise in the discipline with best practices in teaching to create rich learning environments that embrace diversity, promote equity, and integrate research in a manner that challenges students to develop skills and ethics to be leading citizens. For more information about University of Toronto Mississauga and the Department of Language Studies, please visit: https://www.utm.utoronto.ca/ and https://www.utm.utoronto.ca/language-studies/. For more information about the Department of Mathematical and Computational Sciences, please visit https://www.utm.utoronto.ca/math-cs-stats/home. Salary will be commensurate with qualifications and experience. All qualified candidates are invited to apply online by visiting this link: https://jobs.utoronto.ca/job/Mississauga-Assistant-Professor%2C-Teaching-St… Applications must include: * a cover letter * a curriculum vitae * a teaching dossier (including a teaching statement, sample syllabi of previously taught courses & course materials, and teaching evaluations) * two innovative syllabi: a second-year introduction to programming for linguistics students (est. enrollment of 45 students) and one for an upper-year seminar in computer science appropriate for students enrolled in either linguistics or computer science (est. enrollment of 20) * an outline of current and future teaching-related interests * a diversity statement as it relates to teaching, research, and service (as outlined below) * one or two representative samples of teaching-related scholarship (e.g., publication, working paper, or poster) in the area of Computational Linguistics and/or the scholarship of teaching and learning in (Computational) Linguistics. At UTM we are committed to fostering an environment of diversity and inclusion. With an enviable diverse student body, we especially welcome applications from candidates who identify as Indigenous, Black, or racially visible (persons of colour), and who have experience working with teaching or mentoring diverse groups or students. Candidates must demonstrate, in their application materials, an ability to foster diversity on campus and within the curriculum or discipline, and must show evidence of a commitment to equity, diversity, inclusion, and the promotion of a respectful and collegial environment. Candidates must submit a statement describing their contributions to equity, diversity, and inclusion, which might cover topics such as (but not limited to): teaching that incorporates a focus on underrepresented communities; efforts undertaken to develop inclusive pedagogies, collaboration, and engagement with underrepresented communities; and mentoring of students from underrepresented groups. If you have questions about this statement, please contact Professor Salvatore Bancheri, the Chair at langstudies.chair(a)utoronto.ca<mailto:langstudies.chair@utoronto.ca>. Applicants must provide the names and contact information of three referees. The University of Toronto’s recruiting tool will automatically solicit and collect letters of reference from each once an application is submitted. Applicants, however, remain responsible for ensuring that referees submit letters (on letterhead, dated and signed) and that the letters are submitted by the referees by the closing date. Submission guidelines can be found at http://uoft.me/how-to-apply. Your CV and cover letter should be uploaded into the dedicated fields. Please combine additional application materials into one or two files in PDF/MS Word format. Questions about this position should be directed to Joanna Szewczyk, Assistant to the Chair at assistant.dls.utm(a)utoronto.ca<mailto:assistant.dls.utm@utoronto.ca>. All application materials, including reference letters, must be received by 11:59pm EDT on November 14, 2022. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority. Diversity Statement The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas. As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP. Accessibility Statement The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission. The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities. If you require any accommodations at any point during the application and hiring process, please contact uoft.careers(a)utoronto.ca<mailto:uoft.careers@utoronto.ca>.

1 0

Kind reminder: NLP Special Issue
by Florentina Hristea 24 Sep '22

24 Sep '22

Special Issue: Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT) MATHEMATICS NEW IMPACT FACTOR 2.592 An Open Access Journal by MDPI link: https://www.mdpi.com/journal/mathematics Guest Editor: * Florentina Hristea, University of Bucharest Deadline for manuscript submissions: April 23, 2023 (Please note that this deadline will not be extended.) Message from the Guest Editor and Special Issue Web page: https://www.mdpi.com/si/mathematics/NLP_HLT A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors<https://www.mdpi.com/journal/mathematics/instructions> page. For further information and questions, please contact: Florentina Hristea University of Bucharest fhristea(a)fmi.unibuc.ro<mailto:fhristea@fmi.unibuc.ro> https://cs.unibuc.ro/~fhristea/

1 0

culturally charged and societally comprehensive multilingual text banks ...
by Albretch Mueller 24 Sep '22

24 Sep '22

marxists.org keeps quite representative text banks in various languages penned by or pertaining to authors in various countries, but for the most part cultured around one theme. They also have texts by and/or about philosophy in general, such as John Locke's: https://www.marxists.org/reference/subject/economics/locke/index.htm Plato, Machiavelli and Aquinas. Something great about those "marxists" is that they don't use javascript crap. Do you know of any other similar sites out there? lbrtchx

1 0

Job: Research Associate -- Metaphor Analysis
by Stefanie Dipper 23 Sep '22

23 Sep '22

Research Associate (m/f/d, fulltime), from Dec 1, 2022 until Jan 31, 2025. Application deadline: Oct 15, 2022 The Collaborative Research Center (CRC) 1475 "Metaphors of Religion" investigates the significance of metaphors for religious meaning making in language use. Together with researchers from religious studies and computational linguistics, the information infrastructure project (INF) "Metaphor Base Camp: Providing the Common Data Basis and Advancing Digital Research Methods for Religious Metaphors" will build the digital research infrastructure for the entire collaboration. Unique research data combined with the possibilities of computer-aided analysis promise previously unknown insights into the use of metaphors across times and cultures. In detail, your tasks include: • Joint research and development in an interdisciplinary research collaboration, • Implementation of cutting-edge use cases from the arts and humanities and • Advancement of modern data management technologies. Personal Qualification • Universtity degree with a major in computational linguistics, corpus linguistics, digital humanities, or a related field • Excellent German and English language skills (written and spoken) • Good presentation and publication skills Ideally, • You have experience in corpus linguistics. We offer: • Challenging and interesting tasks with a high degree of personal responsibility • Team-oriented cooperation in a committed, international and appreciative team We are looking forward to your application! The application (in one PDF file) should mention the reference number ANR 1047 and should include a cover letter, CV, transcript of records, list of publications, and copies of the master's thesis/dissertation and up to three other relevant papers, names and e-mail addresses of two references. The original and legally binding job announcement advertisement in German can be found here: https://jobs.ruhr-uni-bochum.de/jobposting/223502f5e1605285c6eb2f232744279b…. Contact person for further information: Prof. Dr. Stefanie Dipper, stefanie.dipper(a)rub.de

1 0

2025

2024

2023

2022