phd scholarship natural language processing

Fully funded four-year PhD studentships in Natural Language Processing at University of Edinburgh

October 21, 2021

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

Based at the University of Edinburgh: in conjunction with the School of Informatics and School of Philosophy, Psychology and Language Sciences.

Deadlines :

·       Non UK :     26 th  November 2021 ·       UK :            28 th  January 2022

Applications are now sought for the UKRI CDT in NLP’s penultimate cohort of students, which will start in September 2022.

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, machine learning 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.

A wide range of research topics fall within the remit of the CDT:

·       Natural language processing and computational linguistics

·       Speech technology

·       Dialogue, multimodal interaction, language and vision

·       Information retrieval and visualization, computational social science

·       Computational models of human cognition and behaviour, including language and speech processing 

·       Human-Computer interaction, design informatics, assistive and educational technology 

·       Psycholinguistics, language acquisition, language evolution, language variation and change

·       Linguistic foundations of language and speech processing.

The next cohort of CDT students will start in September 2022 with applications being invited now.  Around 12 studentships are available, covering maintenance at the UKRI rate (currently £15,609 per year) plus tuition fees.  

Studentships are open to all nationalities and we are particularly keen to receive applications from women, minority groups and members of other groups that are underrepresented in technology.  Applicants in possession of other funding scholarships or industry funding are also welcome to apply – please provide details of your funding source on your application.

Applicants should have an undergraduate or master’s degree in computer science, linguistics, cognitive science, AI, or a related discipline; or have a breadth of relevant experience in industry/academia/public sector, etc.      

Further details, including the application procedure, can be found at:  https://edin.ac/cdt-in-nlp

Application Deadlines

Early application is encouraged but completed applications must be received  at the latest  by:

26 th  November 2021 (non UK applicants) or 28th January 2022 (UK applicants).

Please direct any enquiries to the CDT admissions team at:  [email protected] .

CDT in NLP Virtual Open Day

Find out more about the programme by attending the PG Virtual Open Week 9 th  November 2021, 2-3pm.  Click here to register:  Computing, Data Science & Informatics | The University of Edinburgh

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Centre for Doctoral Training in Designing Responsible Natural Language Processing PhD

Awards: PhD

Study modes: Full-time

Funding opportunities

Placements/internships

Programme website: Centre for Doctoral Training in Designing Responsible Natural Language Processing

Upcoming Introduction to Postgraduate Study and Research events

Join us online on the 19th June or 26th June to learn more about studying and researching at Edinburgh.

Choose your event and register

Research profile

The Centre for Doctoral Training ( CDT ) in Designing Responsible Natural Language Processing ( NLP ) aims to develop doctoral graduates that represent a new paradigm of interdisciplinary NLP researcher. Individuals, who can harness the full potential of NLP -based systems and create richer interactions between humans and AI . Our training will give students foundational knowledge across five fundamental skills domains, along with expertise in at least one of these areas. The training programme aims to create an NLP practitioner culture of responsibility, with graduates who are confident in combining technical expertise with:

consideration of users and use contexts

Students will train together in cohorts formed from various disciplines and background experiences. The students will be supported to collaborate on “team science” applied NLP projects. Through these projects, the students will also get the chance to work with some of our 70+ partners in the following sectors:

Programme structure

As part of the structure of our PhD with integrated studies, all students must study taught courses whilst completing the research elements of the traditional PhD programme. We have designed the programme to be flexible in the way credits are acquired. However, all students must successfully complete 180 taught credits over the first 3 years, plus the equivalent of 3 years of PhD research, spread over the 4-year programme. Out of the 180 credits, you will achieve 110 via mandatory courses that all CDT students take, and the remaining 70 credits from optional courses at the appropriate SCQF level.

Work placements/internships

Every student will have a placement, no more than 3 months long, during Years 2 or 3. The placements will involve students working in our partner’s teams and using skills developed through their training. We expect around two-thirds of placements to be with industry or in international research labs.

Training and support

Our students will gain the skills, knowledge and experience to study and design real-world applications of NLP in an interdisciplinary training environment, hosted by the new Edinburgh Futures Institute ( EFI ).

The training programme brings together world-leading researchers at our University to supervise students and guide them in their training and learning. Their expertise covers a range of subjects including:

informatics

linguistics

speech science

information science

digital humanities

As well as the formal credit-bearing courses all CDT students will be enrolled on, we have a variety of other training initiatives and activities within the CDT to:

enhance the learning opportunities for students

build links between students across different cohorts

personalise student’s training plans

Examples include:

entrepreneurship and innovation-oriented training

the Annual CDT Festival where students present their work

annual Industry Challenge Days

placements with partners

policy Workshops and Fellowships

responsible NLP speakers and masterclasses

support for student-led training

Students will be involved in the vibrant world-class and interdisciplinary research community at the EFI, with access to cutting-edge computational, design, fabrication and testing facilities across:

the School of Informatics

Edinburgh College of Art

This includes access to multiple state-of-the-art GPU clusters that enable work with large language models.

Career opportunities

We expect many students will seek diverse career pathways after their PhD such as in:

industrial research and development

entrepreneurship and social innovation

creating start-ups

Each cohort of students will participate in Fast-Track Impact training for PhD students in Year 1, and placements will provide students with hands-on industry experience. Students will also be able to participate in Bridge, a new start-up building course delivered by the EFI and partner Codebase.

Additionally, the Bayes Centre and Edinburgh Innovations will provide students with access to University support to facilitate the commercialisation of their research. The support will include:

access to business advisors

accelerators

a network of investors and peers

Entry requirements

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK 2:1 honours degree, or its international equivalent, in an area related to the topic of the CDT, for example informatics, computer science, AI, cognitive science, mathematics, design, linguistics, psychology, philosophy, law, and social and political science.

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
• English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
• Trinity ISE : ISE II with distinctions in all four components.
• PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

• Academic Technology Approval Scheme

If you are not an EU , EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

Fees and costs

Scholarships and funding.

The CDT will be seeking to fund about 10-12 studentships in each cohort, with the ambition to create cohorts of students from different backgrounds and disciplines.

Studentship funding may be subject to eligibility requirements as set by the programme funder. We will conduct eligibility assessments as part of the application process.

For more details, please refer to the programme's webpage.

• Designing Responsible NLP

Search for scholarships and funding opportunities:

• Search for funding

Further information

• CDT Manager
• Phone: +44 (0)131 650 9979
• Contact: [email protected]
• CDT Academic Director, John Vines
• Contact: [email protected]
• CDT in Designing Responsible Natural Language Processing
• School of Informatics
• 10 Crichton Street
• Central Campus
• Programme: Centre for Doctoral Training in Designing Responsible Natural Language Processing
• School: Informatics
• College: Science & Engineering

Select your programme and preferred start date to begin your application.

PhD with Integrated Study Natural Language Processing - 4 Years (Full-time)

Application deadlines.

• How to apply

You must submit two references with your application.

You must submit an application via the EUCLID application portal and provide the required information and documentation.

Application needs to include CV and statement of intent and alignment with the CDT; further details can be found on their website .

Find out more about the general application process for postgraduate programmes:

Joining the Group

The Stanford NLP Group is always on the lookout for budding new computational linguists. Stanford has a great program at the cutting edge of modern computational linguistics.

The best way to get a sense of what goes on in the NLP Group is to look at our research blog , publications , and students' and faculty's homepages . Our research centers around using probabilistic and other machine learning methods over rich linguistic representations in a variety of languages. The group is small, but productive and scientifically focused.

Prospective Graduate Students

Where do you apply for graduate (PhD or MS) study? Not directly to the NLP Group. Stanford graduate admissions are handled through individual departments, so you'll want to apply for admission through either the Linguistics Department or the Computer Science Department . Both departments have excellent graduate programs. Normally, you should apply to the one in which you have more background and greater interest in further study. Do make sure that you emphasize any research experience and results, and that you get letter writers who can speak convincingly about you. Decisions about admissions are made by the department's admissions committee. Because admissions committees represent the whole department and aim to select the best applicants regardless of specialization, you should direct your application towards an appropriately broad audience. And, as you probably know, Stanford admissions are quite competitive.

If you have questions about admissions, please check the graduate admissions web pages listed on the right, or write to the admissions email addresses listed. We NLP Group members attempt to answer specific NLP-related admissions questions (although sometimes we get too busy...), but in general it isn't necessary or helpful to contact us to let us know that you want to apply or have applied for admission.

Current Stanford Students

Are you a student at Stanford and interested in working on a project in NLP? Check out this page for details on how to apply to do research in the group.

Graduate Admissions Resources

Linguistics department, computer science department.

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Language Learning

Phd scholarship: natural language processing and sla.

We are delighted to announce the a three-year PhD position , co-supervised by Professor Amália Mendes (University of Lisbon), Professor Detmar Meurers (University of Tübingen), and Professor Patrick Rebuschat (Lancaster University). It would be great if you could circulate the announcement within your networks.

PhD scholarship: Natural Language Processing and Second Language Acquisition

Applications are invited for a three-year PhD position in Natural Language Processing applied to foreign language learning and teaching at the Linguistics Center of the University of Lisbon ( CLUL ).

The deadline for applications is February 28, 2022 . For additional information, including salary and application details, please visit:

https://euraxess.ec.europa.eu/jobs/733640

The aim of the PhD project is to research, develop and evaluate a digital tool supporting the acquisition of Portuguese as a Foreign or Heritage language. The work can build on the existing ICALL approaches developed at the University of Tübingen for English and German ( http://icall-research.de ). The goal is to support learners in selecting texts that support noticing of key target structures and provide practice opportunities. The computational linguistic analysis can build on recent findings about linguistic structures that are acquired late by heritage speakers of Portuguese and include an empirical validation in the context of the network maintained by the Camões Institute across the globe.

The PhD project will be co-supervised by Professor Amália Mendes (University of Lisbon), Professor Detmar Meurers (University of Tübingen), and Professor Patrick Rebuschat (Lancaster University). The successful applicant will be integrated in the Heritage Language Consortium (HL2C), a strategic partnership between six European universities and the Camões Institute, a branch of the Portuguese Ministry of Foreign Affairs. Further details on the HL2C can be found on our website:

http://wp.lancs.ac.uk/heritage-language/ .

For questions, email us at:

Amália Mendes [email protected]

Detmar Meurers [email protected]

Patrick Rebuschat [email protected]

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• Department of Swedish, Multilingualism, Language Technology
• Doctoral Studies

Natural language processing

Third-cycle programmes in natural language processing provide in-depth methodological and theoretical knowledge of the multidisciplinary research area of language technology.

The focus of this subject is on the development and use of linguistic resources and language-technical tools for the resolution of research questions related to language technology and other disciplines. The programme provides fundamental knowledge of existing such resources and good conditions for the development of new resources, as well as solid experience of addressing language-technological research assignments with a basis in the linguistic resources required for their resolution.

The skills provided by the research programme in natural language processing are becoming increasingly important in the modern information society. They are also increasingly in demand within academia, including in areas other than language technology. The knowledge is applicable within areas such as information searching and other processing of information and texts, such as automatic translation services or search engines . Other examples of important areas of application include machine translation, computer-assisted language learning and corpus linguistics.

A PhD is a prerequisite for a lectureship at a higher education institution.

Supervisors in Natural language processing

The supervisors guides the doctoral student through the doctoral program, both in terms of PhD courses but above all in terms of writing the thesis. The following supervisor in the field of Natural language processing is available:

• Yvonne Adesam

Aleksandrs Berdicevskis

Gerlof Bouma

Dana Dannélls

Markus Forsberg

Dimitrios Kokkinakis

Peter Ljunglöf

Nina Tahmasebi

Shafqat Mumtaz Virk

Elena Volodina

Niklas Zechner

Computer Science and Engineering

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Natural Language Processing Graduate Merit Scholarship

Graduate Student Scholarship - $1,000 ($500 each semester) Scholarship, 1 year total

This competitive scholarship qualifies awardees for in-state tuition.

Awarding Organization: Human Intelligence and Language Technologies Laboratory, Computer Science and Engineering

Department contact: Diana Bergeman ( [email protected] )

Classification: Graduate

Major: Computer Science, Computer Engineering; consideration may be given to closely related majors

Awardees will typically have a Cumulative GPA over: 3.8 (Minimum Cumulative GPA: 3.50)

Other Requirements:

• Applicant's field of study must be artificial intelligence, natural language processing, machine learning, or data science as indicated by your plan of study, your planned/desired internships, and your personal essay or research statement.
• Must maintain a cumulative GPA of at least 3.5 for courses taken at UNT
• Must not be on academic probation
• Must meet UNT Toulouse Graduate School and Departmental Admission Guidelines

Advertising: Aug 1, 2019 – July 31, 2020

Applications Open: Aug 1, 2020

Award Date: Fall 2020

Scholarships and jobs in Natural Language Processing

PhD scholarship in Natural Language Processing for Sign Languages

Job Information

Offer description.

The field of Natural Language Processing (NLP) has considerably advanced in recent years thanks to a paradigmatic shift caused by the availability of massive amounts of text, deep learning models  and powerful computational resources. NLP technology is currently  available for many domains and languages, notably translation between spoken languages has considerably evolved. However, where Sign Languages (SLs) are of concern it is fair to say that NLP is in its infancy. There are many reasons for the current situation due to the specific characteristics of  SLs and to the lesser availability of resources for most SLs.  The field of SL processing has long been the concern of computer vision research: tasks such as  sign language detection, sign language identification, sign language segmentation have all been addressed within a computer vision paradigm.  However, given that SLs are natural languages, we firmly believe that a multi-disciplinary  approach which includes linguistics and computational linguistics research  in addition to computer vision should be considered.   The area of Natural language processing for SLs aims to analyse sequences of signs in order to, for example, associate lexical categories to signs or disambiguate them or establishing dependency relations between them to produce linguistically rich representations to support for example the identification of specific types of information in signed utterances (e.g. who did what to whom, when, and how).  In general, non-visual representations of SLs have been adopted in order to support the above mentioned processes, that is symbolic,  instead of video-based representations are used to further analyse the output of computer vision processes. Such representations could be automatically produced if high quality data-sets were available for training NLP approaches.  Departing from  our work on Sign Language Translation in the proposed project aims at addressing with an interdisciplinary team the following objectives:

•  Implement data collection, linguistic annotation, and data augmentation mechanisms to increase the availability of SL resources
•  Investigate current architectures for Sign Language Recognition (SLR) and adapt them to available datasets
•  Develop Natural Language Processing (e.g. PoS tagging, parsing, sense disambiguation) for the languages of the project
•  Adopt hybrid approaches to Sign Language Translation combining Machine Learning and Linguistic Information
•  Implement technological demonstrators such as for example Information Extraction for SLs. The hired PhD will work on some of those objectives.

This position includes a teaching commitment load of 45 hours per academic year.

Natural Language Processing for Sign Languages,  Sign Language Recognition,  Sign Language Translation, and Sign Language Processing Applications

Requirements:

• Bachelor degree in Computer Science or Linguistics
• Master in Computational Linguistics, Natural Language Processing, Machine Learning,  Linguistics with solid mathematical or computational background
• Knowledge of current Deep Learning techniques in Natural Language Processing, Machine Learning, and Statistics is desirable
• Admission to the PhD program of the Department of Information and Communication Technologies at UPF is a prerequisite to enjoy the contract.

This project is carried out in collaboration with the LSC Lab (Laboratori de llengua de signes catalana) of the Department of Translation and Language Sciences, UPF. The candidate will be co-supervised by Prof. Horacio Saggion and Prof. Josep Quer.

Starting date (planned):  1/10/2023

Application deadline:  30/06/2023

Gross monthly salary: 1680€. (To increase to 2020€ during the fourth year of the PhD).

This position is co-funded by the PhD fellowship program of the Department of Information and Communication Technologies at Universitat Pompeu Fabra (DTIC-UPF), and the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond. Its benefits and conditions are available at: https://www.upf.edu/web/etic/predoctoral-research-contracts .

More information about the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond: https://www.upf.edu/web/mdm-dtic .

If you are interested in this position please send:

• a motivation letter
• (max)  4-page updated CV
• optionally, names of referees (or reference letters)

to: Prof. Horacio Saggion, LaSTUS lab  / TALN research group, Universitat Pompeu Fabra. [email protected] In your mail clearly indicate your interest for the position “Natural Language Processing for Sign Languages”.

Requirements

Additional information, work location(s), where to apply.

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Zhanna Sarsenbayeva, University of Melbourne

Abdulsalam Ometere Latifat, African University of Science and Technology Abuja

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Blake Woodworth, Toyota Technological Institute at Chicago

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Xuanyi Dong, University of Technology Sydney

Yu Liu, Chinese University of Hong Kong

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Ananya Sai B, IIT Madras

Byeongchang Kim, Seoul National University

Daniel Patrick Fried, UC Berkeley

Hao Peng, University of Washington

Reinald Kim Amplayo, University of Edinburgh

Sungjoon Park, Korea Advanced Institute of Science and Technology

Ajith Suresh, Indian Institute of Science

Itsaka Rakotonirina, Inria Nancy

Milad Nasr, University of Massachusetts Amherst

Sarah Ann Scheffler, Boston University

Caroline Lemieux, UC Berkeley

Conrad Watt, University of Cambridge

Umang Mathur, University of Illinois at Urbana-Champaign

Amy Greene, Massachusetts Institute of Technology

Leonard Wossnig, University College London

Yuan Su, University of Maryland at College Park

Amir Gilad, Tel Aviv University

Nofar Carmeli, Technion

Zhuoyue Zhao, University of Utah

Chinmay Kulkarni, University of Utah

Nicolai Oswald, University of Edinburgh

Saksham Agarwal, Cornell University

Emmanouil Zampetakis, Massachusetts Institute of Technology

Manuela Fischer, ETH Zurich

Pranjal Dutta, Chennai Mathematical Institute

Thodoris Lykouris, Cornell University

Yuan Deng, Duke University

Ella Batty, Columbia University

Neha Spenta Wadia, University of California - Berkeley

Reuben Feinman, New York University

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Gierad Laput, Carnegie Mellon University

Mike Schaekermann, University of Waterloo

Minsuk (Brian) Kahng, Georgia Institute of Technology

Niels van Berkel, The University of Melbourne

Siqi Wu, Australian National University

Xiang Zhang, The University of New South Wales

Abhijeet Awasthi, Indian Institute of Technology - Bombay

Aditi Raghunathan, Stanford University

Futoshi Futami, University of Tokyo

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Qian Yu, University of Southern California

Ravid Shwartz-Ziv, Hebrew University

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Shuang Liu, University of California - San Diego

Stephen Tu, University of California - Berkeley

Steven James, University of the Witwatersrand

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Antoine Miech, INRIA

Arsha Nagrani, University of Oxford

Arulkumar S, Indian Institute of Technology - Madras

Joseph Redmon, University of Washington

Raymond Yeh, University of Illinois - Urbana-Champaign

Shanmukha Ramakrishna Vedantam, Georgia Institute of Technology

Lili Wei, Hong Kong University of Science & Technology

Rizanne Elbakly, Egypt-Japan University of Science and Technology

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Anne Cocos, University of Pennsylvania

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Rotem Dror, Technion - Israel Institute of Technology

Shikhar Vashishth, Indian Institute of Science - Bangalore

Yang Liu, University of Edinburgh

Yoon Kim, Harvard University

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Imane khaouja, Université Internationale de Rabat

Aayush Jain, University of California - Los Angeles

Gowtham Kaki, Purdue University

Joseph Benedict Nyansiro, University of Dar es Salaam

Reyhaneh Jabbarvand, University of California - Irvine

Victor Lanvin, Fondation Sciences Mathématiques de Paris

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Sangeun Oh, Korea Advanced Institute of Science and Technology

Shuo Yang, Shanghai Jiao Tong University

Bidisha Samanta, Indian Institute of Technology Kharagpur

Ekaterina Vylomova, The University of Melbourne

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Tim Rocktaschel, University College London

Romain Gay, ENS - École Normale Supérieure

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Ahmed M. Said Mohamed Tawfik Issa, Georgia Institute of Technology

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Arvind Satyanarayan, Human-Computer Interaction, Stanford University

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Doctoral student in Funded PhD position in Natural Language Processing

University of gothenburg.

The University of Gothenburg tackles society’s challenges with diverse knowledge. 53 500 students and 6 500 employees make the university a large and inspiring place to work and study. Strong research and attractive study programmes attract scientists and students from around the world. With new knowledge and new perspectives, the University contributes to a better future.

Job assignments

The chosen candidate will devote their time primarily to research to be presented in a doctoral dissertation and to obligatory course work. S/he may, however, undertake a limited amount of teaching, administration or research not directly connected to their dissertation topic.

 General entry requirements

To meet the basic entry requirements of doctoral programmes at the University of Gothenburg, applicants must have obtained a second-cycle degree, have completed studies of at least 240 higher education credits of which at least 60 credits were awarded in the second-cycle, have completed a corresponding programme in some other country or be able to demonstrate the possession of equivalent qualifications.

Specific entry requirements, Natural language processing :

At least 30 credits from second-cycle courses in Computational linguistics, Language technology, or Natural language processing, including a thesis of at least 15 credits;

or  at least 30 credits from second-cycle courses in linguistics, including a thesis of at least 15 credits, plus at least 30 credits from first-level courses in Language technology, Computational linguistics, Natural language processing, or Computer science;

or  at least 30 credits from second-cycle courses in Computer science, including a thesis of at least 15 credits, plus at least 30 credits from first-level courses in linguistics.

A good reading and speaking knowledge of English is necessary. Knowledge of Swedish is not a requirement.

Regulations for the evaluation of qualifications for education on a doctoral level are given in Higher Education Ordinance, SFS 1998: 80.

For further information regarding the position

Information about the PhD program in NLP and our research unit:  [email protected]

General information about PhD studies and the application process: Stina Ericsson, Associate head of department for doctoral studies ( [email protected] )

Type of employment: Fixed-term employment, 4 years Basis: 100% Location: Department of Swedish, University of Gothenburg First day of employment: 2022-02-01

Union representatives at the University of Gothenburg: https://www.gu.se/en/about-the-university/work-at-the-university-of-gothenburg/how-to-apply

Information for International Applicants

Choosing a career in a foreign country is a big step. Thus, to give you a general idea of what we and Gothenburg have to offer in terms of benefits and life in general for you and your family/spouse/partner please visit:

https://www.gu.se/en/about-the-university/welcome-services

https://www.movetogothenburg.com/

How to apply

In order to apply for a position at the University of Gothenburg, you have to register an account in our online recruitment system. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline. The selection of candidates is made on the basis of the qualifications registered in the application.

See also the document  Practical information to applicants:   https://www.gu.se/en/swedish/practical-information-to-applicants-for-phd-position-in-natural-language-processing  

Closing date:  Friday, October 1 at 24:00 is the last time to send in the application.

The University of Gothenburg promotes equal opportunities, equality and diversity.

Applications will be destroyed or returned (upon request) two years after the decision of employment has become final. Applications from the employed and from those who appeal the decision will not be returned.

In connection to this recruitment, we have already decided which recruitment channels we should use. We therefore decline further contact with vendors, recruitment and staffing companies.

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• UTCS Direct

UT Computer Science Students Win Prestigious NSF Graduate Research Fellowships

Submitted by Staci R Norman on Mon, 05/20/2024 - 10:00am

The National Science Foundation (NSF) has announced the recipients of its prestigious Graduate Research Fellowships (NSF GRFP) for 2024, and students from the Department of Computer Science at The University of Texas at Austin's College of Natural Sciences (CNS) have been prominently recognized. This year, four Computer Science students were honored with fellowships or honorable mentions, highlighting their outstanding contributions and potential in various cutting-edge research areas.

Fellowship Recipients

• Leo Orshansky, undergraduate student  - Unconventional Computing, Quantum Computing
• Stephane Hatgis-Kessell, undergraduate  - Artificial Intelligence

Honorable Mentions

• Arthur King Zhang, graduate student - Robotics
• Jason Ho, graduate student - Computer Architecture
• Jacob L. Block, graduate student - Machine Learning

College-Wide Recognition

Aside from the accomplishments within the Computer Science department, a total of six undergraduate students and nine graduate students across various departments within the College of Natural Sciences were awarded NSF Graduate Research Fellowships. Additionally, two undergraduates and twelve graduate students received honorable mentions. These awardees represent a broad spectrum of research fields, including Mathematics, Chemistry, Physics, Biology, Marine Science, and Astronomy.

In total, 15 students from seven different departments within CNS were honored with fellowships, underscoring the diverse and high-caliber research being conducted at The University of Texas at Austin.

About the NSF Graduate Research Fellowship Program

The NSF GRFP is a highly competitive program that supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics (STEM) disciplines. Fellows receive a three-year annual stipend of $37,000 along with a $16,000 cost-of-education allowance, providing significant support to pursue their research at any accredited U.S. graduate institution.

These fellowships not only recognize the exceptional talents and research potential of the students but also contribute to the advancement of knowledge and technological innovation critical to the nation's economic and social well-being. The achievements of the Computer Science students, along with their peers from other departments, highlight the University of Texas at Austin's role as a leader in scientific research and education.

Adapted from an announcement by the College of Natural Sciences .

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