Kettering University

Liverpool John Moores University (LJMU) is a distinctive, unique institution, rooted in the Liverpool City Region and with a global presence. Our students and staff, past, present, and future, are the beating heart of our city and can be found in every corner of every industry and community. We couldn’t exist anywhere else and have shaped the city in which we belong. Working with the people of Liverpool to improve lives and support communities is at the heart of why we were founded and why we exist today. Working in the Faculty of Health, Innovation, Technology and Science's Practice Learning Support Unit, you will work with the PLSU Manager to take responsibility for specific workstreams relating to Practice-based Learning activity in line with the faculty’s strategic objectives around work-based learning and our agenda for student employability. You will provide supervision and support to the function of the Practice Learning Support Unit, which includes the allocation of placements, the processing of placement related documentation as well as ensuring the faculty’s compliance to the University’s Code of Practice for Work based learning and professional, statutory, and regulatory body requirements. You will have previous relevant supervisory and administrative experience, be capable of working collaboratively with stakeholders and people across the University in a professional manner and providing advice and support to students. You will have the ability to communicate well both orally and in writing, competency in IT skills, excellent organisational skills, and the ability to work under your own initiative and as part of a team. Experience of dealing discretely with confidential and sensitive information is essential. For informal enquiries, please contact Sarah Marshall at s.marshall@ljmu.ac.uk In return, we offer an excellent benefits package including generous annual leave entitlement, pension scheme, induction and development support as well as family-friendly policies. This is an exciting time to join the university as we deliver the LJMU Strategy 2030 and its vision of LJMU as an inclusive civic university transforming lives and futures, by placing students at the heart of everything we do. If you feel that this is the role you have been looking for and your skills and experience can make a real difference at LJMU, we look forward to hearing from you. LJMU is an equal opportunities employer and welcomes applicants from all backgrounds and communities irrespective of age, transgender status, disability, gender, sexual orientation, ethnicity and religion or belief. All our appointments are made on merit. Please note all of our vacancies will be closed to applications at midnight on the advertised closing date, unless otherwise stated.

The University of Vienna is a community of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. They represent individuals driven by curiosity and a relentless pursuit of excellence. With us, they find the space to try things out and unfold their potential. Are you inspired by their passion and determination? To strengthen our team, we are seeking a university assistant to develop advanced machine learning and artificial intelligence methods. University assistant predoctoral - PhD position in Probabilistic and Interactive Machine Learning 39 Faculty of Computer Science Startdate: 01.10.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.09.2030 Reference no.: 5376 Your personal sphere of influence: The working group “Probabilistic and Interactive Machine Learning” within the research group “Data Mining and Machine Learning” at the Faculty of Computer Science, led by Prof. Sebastian Tschiatschek, develops foundational methods in machine learning and artificial intelligence. We focus particularly on the areas of reinforcement learning, interactive learning, and deep probabilistic models. While modern reinforcement learning (RL) has achieved remarkable success, it remains limited when applied to complex, open-ended, or poorly defined environments. Two of the most critical bottlenecks in contemporary AI are sample inefficiency, often caused by the lack of intelligent, structured exploration, and the "reward engineering" problem, where designing an explicit scalar reward function that captures desired behavior is incredibly difficult or impossible. Furthermore, as AI systems are deployed in more complex environments, the challenges of AI alignment (ensuring systems behave according to human preferences) and constrained learning (adhering to strict safety, legal, or physical boundaries) become important. This position is dedicated to addressing these core challenges by advancing the frontiers of Inverse Reinforcement Learning (IRL), exploration, and safe/aligned AI.                       Your future tasks: You actively participate in research, teaching & administration, which means: You will contribute to academic research projects in the above-mentioned areas. You will work on scientific articles and publish your research results. You will participate in academic conferences to present your research. We expect you to conclude your dissertation agreement within 12 months. You will work on your dissertation and bring it to completion. You will take on teaching responsibilities as specified by the collective agreement. You will support the supervision of students’ projects and theses. You will undertake administrative duties in research, teaching, and university administration, and support the organization of workshops, conferences, and symposia. This is part of your personality: Completed Master's degree (or comparable degree) in computer science, data science, mathematics, communication engineering, or a related field (Applications from candidates close to completion are welcome. Employment can only begin once the Master´s degree has been awarded.) Excellent command of English Outstanding written and oral communication skills Strong ability to collaborate within research teams Perseverance and a proven ability to bring projects to reliable completion High motivation and commitment to scientific excellence Willingness to travel, including participation in national and international conferences Solid knowledge of machine learning and artificial intelligence (in particular in the areas of bandits and reinforcement learning) Strong programming skills, preferably in Python. Experience with deep learning frameworks such as Jax, PyTorch, or TensorFlow. Excellent analytical skills and strong interest in developing a deep understanding of algorithms and methods Cooperative, team-oriented, and proactive working style The following are also desirable: Experience with research methods in the field of machine learning and artificial intelligence, as well as scientific writing Excellent academic record, ideally with initial research results in the area of the position, documented by publications or manuscripts in preparation Experience in university teaching International experience 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. Tenure: The employment duration is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration. It is that easy to apply: Academic curriculum vitae  Letter of motivation including your project ideas (and description of your teaching experience, if available) Abstract of Master´s Thesis Degree certificates Transcript of records List of publications, evidence of teaching experience (if available) If you have any 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: 06/06/2026 Prae Doc

The Role The School seeks to appoint a two-year Career Development Fellow in French & Francophone Studies. Applications are welcome from those working in all areas of French & Francophone literary and cultural studies, and we are especially interested to receive applications from those with specialisms in the 16th, 17th and 18th centuries. In line with institutional expectations for Career Development Fellowships, your time will be divided between the three core activities of teaching (60%), research (30%) and citizenship (10%). The post offers an exciting opportunity to develop internationally excellent research/scholarship and teaching while providing unrivalled, tailored support for your career progression at an exciting and progressive institution. Given the developmental nature of the role it is not anticipated that the post will be extended beyond the initial fixed term of two years. Following the end of the term, the role-holder will be in a strong position to apply for relevant permanent academic roles, should they arise, at Durham or elsewhere. Successful applicants are normally expected to be within 8 years of completing their PhD, although career breaks for parental leave and/or health reasons will be considered. This is because the role is aimed at early career academics who would benefit from a structured development programme and provision of both formal and 'on the job' training. The post is also open to those who are returning to academia after a career in another sector. Successful applicants will ideally be in post by 1 September 2026 or as soon as possible thereafter. The University provides a working and teaching environment that is inclusive and welcoming and where everyone is treated fairly with dignity and respect. Candidates will be expected to demonstrate these key principles as part of the assessment process. Potential Interview Date: TBC, likely late June or early July 2026