Durham University

Salary: £35,608 -£43,482 per annum Contractual hours: 37 Basis: Full Time Package: Full details can be found here - https://www.ucb.ac.uk/about-us/work-for-us/rewards-and-benefits/ Job category/type: Academic, Business, Creative Industries, Further Education Job reference: REQ000519 Attachments: attachmentLecturer - Hairdressing (FE) - JDPS - March 26.pdf Are you a talented hair professional ready to inspire the next generation? We are looking for a passionate and industry-current Hairdressing Lecturer to deliver engaging, hands-on teaching across Level 1–3 programmes within our vibrant commercial salon environment. You will bring up-to-date expertise in cutting, colouring and styling, and inspire students to develop the skills and confidence needed to achieve industry-ready standards. You will have:  Level 3 Hairdressing qualification Strong commercial industry experience Teaching experience (or a willingness to work towards a teaching qualification) A genuine passion for developing and supporting emerging talent If you are ready to share your expertise and inspire our students to succeed in the hairdressing industry, we would love to hear from you.  Standard Information UCB is an equal opportunities employer. We are TEF rated Silver, with a Good Ofsted rating.  UCB has achieved the Investors in Diversity Bronze Award You may need to obtain permission to work in the UK if you don’t already have the unrestricted right to work in any role. There are various types of visa which include the right to work. You can check if you need a UK visa using the Government’s online tool and find information about UK visa routes and the eligibility criteria that must be met for each route Work in the UK - GOV.UK (www.gov.uk). All staff are required to prove their right to work in the UK before they start work. All applicants for employment at the University will be expected to undertake an Enhanced Check with the Disclosure and Barring Service prior to commencement. All applicants for employment at the University will be expected to demonstrate an understanding of the principles of Safeguarding and the PREVENT agenda in the context of further and higher education.

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/PhD Candidate 51 Faculty of Physics Startdate: 01.04.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.03.2030 Reference no.: 5385 We invite you to join the Operational Quantum Information Team (Dakić Group) to work on a theoretical project on the foundations of quantum phase space, focusing on the Wigner–Weyl correspondence and the duality between Hilbert-space and phase-space descriptions. Your personal sphere of influence: As a university assistant (praedoc) in this four-year position, you will be part of the Operational Quantum Information Team led by Professor Borivoje Dakić. Our team is part of the Quantum Optics, Quantum Nanophysics, and Quantum Information group within the Faculty of Physics. We are members of the Vienna Center for Quantum Science and Technology (VCQ), one of the largest quantum hubs in Europe, and of the Austrian Cluster of Excellence (quantA), which promotes basic research in quantum sciences. You will also benefit from being a fellow of the Vienna Doctoral School in Physics (VDSP), joining a thriving community of more than 100 quantum scientists on campus and around 300 quantum researchers in Vienna. The Dakić group is an international team of scientists (Master's, PhDs, and Postdocs). Our expertise encompasses quantum foundations, quantum information theory, and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/. Your future tasks: You will actively participate in research, teaching & administration, which means: You are involved in a well-funded research project on the advertised research topics. You will present your research plan to the faculty and complete a dissertation agreement within 12–18 months. This will be reviewed and adapted on an annual basis. You will work on your dissertation and towards its completion in time. We expect a large degree of independence paired with a high level of social awareness, as the goal will only be achieved in a team. You will contribute to teaching (lab courses, exercise classes) within the provisions of the collective bargaining agreement. You will fulfil some administrative tasks, contributing to the success and self-organization of the group in research, teaching and administration. You will continuously stay informed about the state of the art in your field. You will contribute to outreach by publications, conference presentations and public activities. This is part of your personality: We expect a strong candidate with the following requirements: You have a completed a Master’s degree or Diploma in Physics, specialising in theoretical quantum foundations. You have a proven experience in quantum information theory and quantum foundations, in particular in quantum phase-space methods. You have a proven experience in numerical methods and programming, particularly the simulation of PDEs. Excellent written and spoken English. What we offer: Work-life balance: Our employees enjoy flexible working hours and can partially work remotely. Inspiring working atmosphere: You are part of an international academic team in a healthy and fair working environment. Good public transport connections: Your workplace in the center of beautiful Vienna is easily accessible by public transport. Potential for development: Success in life depends on what you make of it, but if you are ambitious and successful, there are plenty of opportunities to connect you to all relevant top research groups in the world in quantum information, quantum technologies and machine learning. Internal further training & coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses to choose from – free of charge. Fair salary: The basic salary of EUR 3.776,10 (full-time basis, 14x p.a.) increases if we can credit professional experience. 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. Equal opportunities for everyone: We look forward to diverse personalities in the team!   It is that easy to apply: Please send us the following mandatory documents:   Your scientific Curriculum Vitae. Your Bachelor’s and Master‘s degree certificates and transcripts. A Motivation Letter outlining your general motivation for the position and explicitly addressing points 1–4 listed under “This is part of your personality” requirements. Abstract of your Master's thesis. Contact information of two referees. If you have any content questions, please contact: Borivoje Dakic borivoje.dakic@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: 03/30/2026 Prae Doc

About the University 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 Role We are seeking a highly motivated and talented Research Fellow to join an exciting industry-funded applied research project within the Infocomm Technology cluster at SIT. This project addresses the critical challenge of enabling ultra-reliable low-latency communications (URLLC) with priority-aware offloading for public safety services. The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted computation offloading strategies that leverages edge intelligence. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python application programming interfaces (APIs), through rigorous simulations and proof-of-concept (PoC) trials. This position is ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical expressions for latency and offloading probabilities across edge-only, cloud-only, and edge-cloud computation offloading algorithms. Design and develop robust Python APIs for edge-only, cloud-only, and edge-cloud computation offloading algorithms. Set up network testbeds integrating hardware, software, and communication protocols to validate edge-only, cloud-only, and edge-cloud computation offloading algorithms. Conduct rigorous simulations and live PoC trials to ensure computation offloading algorithms meets URLLC targets for public safety services. Participate in and manage the research project with Principal Investigator (PI) to ensure all project deliverables are met. Job Requirements A Ph.D./Master’s degree in Computer Engineering, Communications Engineering, Computer Science, or a highly related discipline. A strong theoretical foundation and research background in wireless communications and edge computing. Strong publication track record in top-tier conferences and journals in the areas of wireless communications, IoT, or edge computing. Demonstrated proficiency in software API and algorithm development of edge intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning techniques is highly advantageous. Experience with theoretical wireless network modelling, particularly stochastic geometry or queuing theory, is highly advantageous.