Seinan Gakuin University

About the University of Bristol: The University of Bristol’s Mumbai campus is an innovative extension of its prestigious UK counterpart, established to promote academic excellence, global collaboration, and impactful industry engagement in one of the world’s most dynamic financial hubs. The campus offers a truly international and cross-cultural academic environment, combining the University’s rigorous academic traditions with the fast-evolving Indian and South Asian business landscape. As part of a growing global network, the Mumbai campus serves as a hub for interdisciplinary learning, applied research, and thought leadership. Faculty members enjoy access to global partnerships, collaborative research opportunities, and the ability to contribute to high-impact academic programmes that prepare students to meet the challenges of an interconnected world. Role Overview We are seeking a highly motivated and accomplishedAssociate Professor in Data Science / Statistics to join our growing academic team at the University’s Mumbai Enterprise Campus (MEC), a flagship transnational education initiative of the University of Bristol set to open its doors in 2026, coinciding with the University’s 150th anniversary. The role will lead on curriculum and research development, mentor junior faculty, and strengthen the University’s academic reputation through teaching, innovation, high-impact research and external engagement. The role will report to the Mumbai Enterprise Campus Associate Dean and have dotted lines reporting to and collaborating with the Head of School of Mathematics in the Faculty of Science and Engineering (S&E), and other relevant University senior leaders. This senior-level position is ideal for candidates with a strong academic profile, proven research excellence, and leadership capabilities within higher education. Key Responsibilities Leadership, Culture, and Engagement Provide distinguished academic leadership and operational capacity in both teaching and research within mathematical statistics or data science fostering an environment of scholarly excellence. Represent the university at national and international academic and professional forums, enhancing its visibility and reputation. Establish and nurture strategic partnerships with industry, academic institutions, and government agencies to support research initiatives and enrich the learning environment. Contribute substantively to departmental governance, administration, and operational delivery in close collaboration with senior leadership. Develop, coordinate, and deliver advanced undergraduate and postgraduate courses in areas ranging from mathematical statistics and machine learning to coding and algorithmics. Mentor junior and early-career faculty members, actively participating in their professional development and performance evaluation processes. Work collaboratively with Professional Services partners to deliver delegated administrative and governance duties. Teaching Play a pivotal role in curriculum development and contextualisation, programme accreditation, programme enhancement and the maintenance of quality assurance standards within the university. Create a nurturing environment for excellence across the whole education offer. Promote innovative teaching strategies that improve student experience, transferable skills creation and employability prospects. Research Maintain a sustained record of high-quality outputs in excellent, peer-reviewed academic publications or in other recognised  discipline specific channels, actively contributing to the international discourse in mathematical statistics and data science. Initiate, lead, and secure funding for research projects with significant potential to contribute to scholarly knowledge and societal advancement. Provide support for other academic disciplines and interdisciplinary opportunities within the MEC. Ensure research strengths are matched and leveraged to develop teaching excellence and integrated in MEC programme offerings. Where applicable, supervise and mentor doctoral, postgraduate, and undergraduate research projects, ensuring the highest standards of academic rigor and integrity. Essential Criteria A completed PhD in mathematical statistics and data science. Significant academic experience, including substantial post-PhD teaching and research achievements Proven academic leadership experience in teaching (e.g., programme coordination, research leadership, or departmental roles). Demonstrated excellence in teaching, curriculum design, and academic mentorship. Strong publication record in highly ranked peer-reviewed international publications, or in recognised equivalent discipline-specific channels, and ability to contribute to the school’s strategic research ambition to address global/societal challenges of our time. Evidence of excellent organisational and administrative skills. Excellent interpersonal, communication, and collaborative leadership skills. Demonstrates a proactive approach to professional development. Strong programming expertise. Expertise is machine learning and data science. Desirable Criteria At least 10-12 years of academic experience post-PhD Track record of securing or leading externally funded research projects. Willingness and strong evidence to seek collaborations/engage with public, private and not-for-profit/charitable sectors. Experience supervising doctoral students A teaching qualification recognised by the UK Higher Education Academy or equivalent. For any concerns regarding your application, kindly reach out to us at careers@daskalos.com

At the University of Vienna, almost 11,000 people work together on the big questions of the future. Approximately 7,700 of them are academic staff members. These are individuals who, with their curiosity and their continuous pursuit of excellence, engage in international cutting-edge research and teaching. With us, you will also find space to unfold your potential. We are looking for a/an ... Scientific project assistant predoctoral 37 Faculty of Business, Economics and Statistics Startdate: 01.06.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Reference no.: 5569 Explore and teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our accomplished team! Your personal sphere of influence: The research group Statistical Analysis and Inference (STAIN) (https://stain.univie.ac.at/) at the Department of Statistics and Operations Research (https://isor.univie.ac.at/), University of Vienna, is offering a PhD position in the area of Mathematical Statistics and Machine Learning, starting at the earliest possible date, and no later than September. The doctoral position is funded within the framework of a FWF project and is part of the DFG–FWF Research Unit 5381 (https://for5381.uni-freiburg.de/en/research-unit-5381/). The Research Unit offers a highly stimulating and collaborative research environment, including numerous scientific events, workshops, and joint activities, from which early‑career researchers benefit particularly strongly. Your future tasks: You actively participate in research and administration, which means: In collaboration with your advisor you will develop and complete your own PhD-project in the area of mathematical statistics or the theory of machine learning. We expect you to finalize your dissertation agreement (= project proposal) within 12-18 months and that you regularly participate in the PhD seminar. You get acquainted with a recent research area, formulate new and relevant mathematical problems and solve them independently under the supervision of your PhD advisor. You take on administrative tasks in research. You work on your own publications, academic articles and presentations. This is part of your personality: Completed Master's degree or diploma, in the field of statistics, mathematics or a closely related field (completed or about to be completed). A strong background in mathematical statistics and probability theory. Enthusiasm for abstract mathematical problems and strong tenacity and patience in solving them. Active interaction and discussion with the colleagues from the research group and the department. Programming skills. Didactic competences / experience with e-learning. Excellent command of written and spoken English (C1 Level). What we offer: The employment duration is 3 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 3 years if the employer does not terminate it within the first 12 months by submitting a declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. 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. Equal opportunities for all: We welcome every additional/new personality to the team! It is that easy to apply: With your scientific curriculum vitae. With a letter of intent outlining especially your previous experience with mathematical problem solving (max. 1 page). With your summary of research interests (max. 1 page) With contact details of at least two persons who agreed to provide reference letters upon request. Transcripts and Master Degree  /  Diploma (if available). Via our job portal/ Apply now -  button. If you have any questions, please contact: Johannes Moritz Jirak moritz.jirak@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: 05/11/2026

Lecturer (Early Career Faculty)/ Assistant Professor - Economics / Applied Economics/ International Trade Victoria University, Delhi NCR Location: Victoria University, Gurugram, Delhi NCR Job Level: Level A/ B Employment Type: Full-Time About the University Victoria University (VU) is establishing its first India campus in Gurugram, Delhi NCR, scheduled to open in mid-2026. Victoria University (VU) is a leading dual-sector tertiary institution based in Melbourne, Australia, offering both higher education and TAFE programs. With over 42,000 students globally, including a strong international presence, VU operates across multiple campuses in Melbourne, Sydney, and partner institutions in Asia (Sri Lanka and China). Guided by its strategic plan “Start Well, Finish Brilliantly 2022–2028”, the University aims to become a global leader in dual-sector learning and impactful research. VU is known for its industry partnerships, innovative teaching models, and commitment to creating career-ready graduates while fostering inclusive and future-focused education. In India, Victoria University (VU) is establishing its first India campus in Gurugram, Delhi NCR, scheduled to open in mid-2026, delivering industry-aligned programs with a global curriculum. The Gurugram campus focuses on emerging technologies such as Artificial Intelligence, Data Science, and Information Technology, providing students with international exposure, practical learning, and strong industry connections. Position Summary The Lecturer (Early career faculty)/ Assistant Professor will serve as a Teaching Focused Academic (TFA) within the Victoria University Business School, contributing significantly to teaching, curriculum development, and academic coordination in business-related disciplines. The role focuses on delivering high-quality teaching across undergraduate and postgraduate programs, particularly in economics, international trade, and international business. The appointee will create an engaging and collaborative learning environment, integrate industry practices into teaching, and contribute to curriculum innovation. Additionally, the candidate will be expected to contribute to research and scholarship, including publishing in quality journals and supporting the University’s research impact agenda. Indicative Areas of Expertise Economics / Applied Economics International Trade International Business Global Markets & Trade Policy Business Analytics & Economic Analysis Industry Engagement & Experiential Learning Key Responsibilities Deliver business and economics subjects at undergraduate and postgraduate levels Design and develop industry-relevant curriculum and learning materials Facilitate teaching in face-to-face, online, and blended modes Assess student learning and provide timely, constructive feedback Contribute to curriculum development and subject/unit coordination Engage with industry to enhance practical and real-world learning Conduct research and publish in high-quality academic journals Contribute to Scholarship of Teaching and Learning (SoTL) initiatives Support supervision of postgraduate research students (Masters/PhD) Participate in academic administration, moderation, and validation activities Provide academic support to international partnerships Ensure compliance with academic policies and quality standards Qualifications & Selection Criteria Essential Assistant Professor: PhD (or equivalent) in Economics, Applied Economics, International Trade, or a related discipline Lecturer (Early Career Faculty): Master’s degree and/or currently a PhD candidate in Economics, Applied Economics, International Trade, or a related discipline Demonstrated excellence in teaching across face-to-face, online, or blended modes Strong expertise in economics, international trade, or international business Experience in curriculum development and subject coordination Evidence of research publications in quality academic journals Ability to contribute to industry-engaged teaching and learning Strong communication, interpersonal, and organisational skills Commitment to teaching excellence and university vision Desirable Recent industry experience in relevant sectors Proficiency with business and analytical tools Application Requirements Applicants are required to submit with the following documents: Resume (Curriculum Vitae) Cover Letter If you experience any issues while submitting your application, please email: career@daskalos.com