本期为大家推荐瑞典皇家理工大学、阿姆斯特丹大学最新2025岗位制博士项目信息。
瑞典皇家理工大学
Doctoral student in learning and optimization for edge computing
KTH | School of Electrical Engineering and Computer Science
APPLICATION DEADLINE: 3 Feb 2025
Project description
Third-cycle subject:Computer science
The Division of Network and Systems Engineering is looking for one doctoral students with a very strong background and interest in system modeling, optimization, and machine learning. The successful candidate will join a MSCA Doctoral Network project on developing rigorous, novel tools for safe and prompt learning and optimization of distributed network infrastructures. A strong focus will lie on the development of algorithms that include machine learning components, and on cooperation with industrial partners and with theTECoSAcompetence center at KTH.
The Division of Network and Systems Engineering conducts fundamental research in networked systems, wireless communications and cyber security. Industrial projects involve partners such as Ericsson, Atlas Copco and Telenor. Part of the research is conducted within the framework of theWallenberg AI, Autonomous Systems and Software Programand inDigital Futures. We have an extensive academic network and collaborate with researchers at MIT, UIUC, Stanford, EPFL, among others.
Supervision:Prof. Viktoria Fodor and Prof. György Dán
What we offer
- The possibilityto study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.
- A workplacewith many employee benefitsand monthly salary according toKTH’s Doctoral student salary agreement.
- A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
- Help torelocate and be settled in Sweden and at KTH.
Admission requirements
To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
- passed a second cycle degree (for example a master's degree), or
- completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
- acquired, in some other way within or outside the country, substantially equivalent knowledge
Applicants must not have resided or carried out their main activity (work, studies, etc.) in Sweden for more than 12 months in the 36 months immediately before the date of recruitment, due to the MSCA DN mobility rules.
In addition to the above, there is also amandatory requirement for English equivalent to English B/6.
Selection
In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:
- independently pursue his or her work
- collaborate with others,
- have a professional approach and
- analyse and work with complex issues.
In the evaluation of candidates, great emphasis is placed on study results and completed courses. An earlier specialization in optimization and/or mathematical statistics and/or machine learning is highly desirable and especially meritorious.
Applicants are expected to be able to read and write scientific texts in English, as well as being able to communicate verbally in Swedish, as it is demanded in the everyday work.
After the qualification requirements, great emphasis will be placed on personal competency.
Target degree:Doctoral degree
Information regarding admission and employment
Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years ' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.
To apply for the position
Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).
Applications must include:
- CV including your relevant professional experience and knowledge.
- Copies of diplomas and grades from previous university studies and certificates of fulfilledlanguage requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages. Copies of originals must becertified.
- Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.
阿姆斯特丹大学
PhD Position in Archives of Deep Learning
uva | Faculty of Humanities
Salary:€2,901 - €3,707
APPLICATION DEADLINE:20 Feb 2025
This is what you will be doing
The PhD student will be part of a 5-year European Research Council Advanced grant: 'Deep Culture - Living with Difference in the Age of Deep Learning'. They will also collaborate with other PhD students working on AI methodologies for cultural-historical research. The 'Deep Culture' project investigates the relations between deep learning and global cultural production and consumption. Deep-learning technologies like ChatGPT have generated a lot of excitement about our new relations with AI but have also provoked much public anxiety. The effects on cultural production, creativity as well as the use of cultural archives to feed models have been key concerns. The project coins 'deep culture' to describe the global transformations that deep learning has brought on culture, and how culture is, in turn, key to deep learning.
This PhD will focus on questions of historical archives and deep learning. We are interested in specific historical archives that like the Holocaust archives are controversial for their relationships to deep learning models. There are many historical archives that bring together very large data, both textual and multimedia, but their online publication and integration in further processing has often led to widely publicized controversies as in the example of theDutch online archives of Nazi collaborators. The ingestion of archives like historical medical archives have led to gendered and racialized health recommendations by language models, while Holocaust archives have been targeted by online misrepresentations and Holocaust denial in language and image models. Deep-learning models must first pass the 'Nazi test' and have safeguards against Holocaust denial, producing curated answers for anything its creators see as ethically complicated.
We envisage that the applicant will have a range of diverse skills that complement each other. While firmly grounded in knowledge in AI, digital methods and digital humanities he/she should also be open to qualitative methods and historical-cultural analysis. We expect that you are willing to critically engage with interdisciplinary approaches and methodologies, learn from each other in the team and add new skills and practices to your existing interests. The project wants to avoid hierarchies and specialisations as far as possible.
This is what we ask of you
You are expected:
- to conduct research drawing on approaches from deep learning, digital methods and digital humanities;
- to publish and present research results in leading international conferences and journals;
- to complete a PhD thesis to be submitted within the period of appointment;
- to participate in project meetings and relevant networks;
- to participate in knowledge dissemination activities with academic and other stakeholders;
- to contribute to our teaching activities as assistant to courses and/or guiding students in their thesis work
- to contribute to the organisational and administrative work on the project