Leeds Trinity University

At the University of Vienna almost 11,000 personalities work together towards answering the big questions of the future. Around 7,700  of them do research and teaching, around 3,000 work in administration and organisation. We are looking for a/an University assistant predoctoral - Completion contract 39 Faculty of Computer Science  Startdate: 01.07.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.12.2026 Reference no.: 5591 Your personal sphere of influence: The Doctoral School Computer Science DoCS (https://docs.univie.ac.at) founded in 2020 offers a structured doctoral programme that supports early-career research in the areas of Computer Science and Business Informatics. The programme includes world-leading researchers in Computer Science as supervisors and co-supervisors who pursue basic as well as applied research within an intensive doctoral teaching programme tailored to the scientific needs of students in small topical groups. The Doctoral School Computer Science provides the option of performance-based contract extensions (up to 6 months) to doctoral students who previously had a position as university assistant (prae doc) or as third-party funded researcher (prae doc) at the University of Vienna. This completion contract is meant to support a high-qualified DoCS fellow in the final phase of the doctoral studies. The successful applicant will get a contract as university assistant (prae doc) and will be working in the research group Data Mining and Machine Learning at the Faculty of Computer Science. Your future tasks: You actively participate in research, teaching & administration, which means: Your main task is to complete and to submit your dissertation. You meet and consult with your supervisor(s) on a regular basis in order to priorities your tasks and get the necessary support to complete your dissertation successfully in time. You participate in your department's and the Doctoral School activities and workshops. You participate in research projects and research studies. You participate in creating publications, academic articles, and  presentations. You participate in conferences and publish research results in peer-reviewed journals. You actively seek support (workshops, training, coaching etc.) if necessary. You supervise project work of students. You participate in teaching and independent teaching of courses as defined by the collective agreement. This is part of your personality: General expectations:  Master's degree in one of the following subjects: Science and Technology Studies, Human-Computer Interaction, Information Science, Communication Science or a related field. Excellent academic and study record (e.g. FÖP within one year, annual submission of progress reports; outstanding publication record etc.). Excellent and up-to-date knowledge of Reinforcement Learning, Hyperbolic Geometry for Reinforcement Learning, and Plasticity in Neural Networks (evidenced by recent publications in A/A* conferences or Q1-WoS journals). Excellent and up-to-date knowledge of typical machine learning and deep learning frameworks such as PyTorch and Jax (evidenced by relevant recently published source code or similar). Experience in university teaching. High level of motivation and commitment to succesfully complete your doctoral studies within the anticipated time frame. High level of written and oral communication skills. Excellent command of written and spoken English (C1 Level) Experience with the structures of the University of Vienna. Member of the UniVie Doctoral School Computer Science DoCS. Desireable qualifications: B2-level German skills International experience Collaborative, team-minded and pro-active attitude. What we offer: Work-life balance: Our employees enjoy flexible working hours and can partially work remotely. Inspiring working atmosphere: You are a part of an international academic team in a healthy and fair working environment. Good public transport connections: Your workplace is easily accessible by public transport. Internal further training & Coaching: Opportunity to deepen your skills on an ongoing basis. There are over 600 courses to choose from – free of charge. Fair salary: The basic salary of EUR 3.776,10 (on a full-time basis) increases if we can credit professional experience. It is that easy to apply: With your scientific curriculum vitae Cover letter/letter of motivation referring to the job posting, especially regarding the required skills for the position, and the goal of completing the dissertation within 6 months of starting employment. With your summary of research interests / ideas for a prospective doctoral project proposal (max. 3 p.) With a plan for the completion of the doctorate: current state, work packages, and timeline until submission of your thesis, including milestones. With a confirmation of enrolment in the doctoral programme  List of publications If you have any content questions, please contact: Sebastian Tschiatschek sebastian.tschiatschek@univie.ac.at We look forward to new personalities in our team! The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity. We place particular emphasis on enhancing women’s representation among the academic and general university staff, particularly in leadership roles, and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 05/16/2026 Prae Doc

Ref.: 536196 Work type: Full-time Department: Centre for Information Technology in Education within the Faculty of Education (10001) Categories: Senior Research Staff & Post-doctoral Fellow Post-doctoral Fellow / Senior Research Assistant in the Centre for Information Technology in Education within the Faculty of Education (Ref.: 536196).  To commence from as soon as possible to 31 August 2027. Applicants should possess a doctoral or master’s degree in Education, the Learning Sciences or a related discipline, with a strong background in technology-enhanced learning and assessment, learning design and/or learning analytics for teacher learning and pedagogical innovation. Familiarity with theory and pedagogical implementation of digital literacy and AI literacy in the curriculum, and leadership for technology-enhanced learning and pedagogical innovation is crucial. Preference will be given to those with expertise in innovative methods of assessment and/or advanced statistical methods, such as multiple linear regression, mediation analysis, multilevel modeling, and/or latent variable models. Experience in managing and analyzing large datasets and in using generative AI tools for research purposes is a relevant strength. The appointee should also have a high level of writing proficiency for academic purposes and for a general audience in both English and Chinese. Good organizational skills, the ability to work independently and interdependently within a team on a tight schedule are essential to succeed in this role. Applicants in the final stages of their PhD studies may be considered for appointment as a Senior Research Assistant. The appointee will work closely with the Principal Investigator and the project team, to conduct research in a project on “An ecological evolutionary approach to curriculum development and teacher learning for artificial intelligence literacy”. He/She will take a key role in all stages of the research, including but not limited to (a) research and instrument design; (b) data collection, management, analysis and interpretation; (c) preparation for publications in peer reviewed journals and books; (d) working collaboratively with other project team members to liaise with schools, professional bodies and other relevant stakeholders; and (e) providing assistance in administrative and dissemination work. The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date CV. Review of applications will start as soon as possible and continue until June 30, 2026, or until the post is filled, whichever is earlier.

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The primary responsibility of this role is to deliver on an industry funded research project where you will be part of the research team to develop advanced Nuclear Quadrupole Resonance (NQR) scanning technologies. Job Responsibilities Participate in research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: i. Conduct comprehensive literature review on NQR technology, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system architecture, component selection, and experimental setup. iii. Conduct lab and site tests to evaluate the performance of NQR prototype. iv. Generate design documents and test documents at every stage of development until the final prototype. Coordinate procurement and liaison with vendors/suppliers. Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations.  Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements Has a bachelor’s degree in Electronics; Specialization in RF/Microwave or Digital Signal Processing (DSP) is preferred. Has solid knowledge of electronics, including circuit design, PCB layout, and hardware troubleshooting. Has relevant competence in the areas of Radio Frequency design and measurement; Familiarity with high-power amplifier systems is a plus. Has relevant experience in the areas of Digital Signal Processing (DSP) design and implementation is a plus. Experience in electromagnetic simulation using CST Studio Suite and circuit simulation with PSpice, LTspice, or equivalent is an advantage. Experience with developing software for instrument control and data acquisition using LabVIEW is helpful. Key Competencies Strong experimental skills and theoretical knowledge in electronic circuits, and RF or DSP design, with the ability to apply hands-on prototyping and system development. Able to build and maintain strong working relationships with people within and external to the university. Self-directed learner who believes in continuous learning and development Proficient in technical writing and presentation  Possess strong analytical and critical thinking skills Show strong initiative and take ownership of work