Purwokerto Muhammadiyah University

Head of Academic Division- Full-Time Position Position Overview The Cordon Bleu Paris Institute is seeking highly qualified and dynamic individuals to play a significant role in overseeing and shaping its strategic academic directions aiming for continued disciplinary excellence and successful student-centred outcomes. The successful candidate will demonstrate proven experience in higher education teaching, academic quality assurance compliance requirements, programme management and development in the areas of Hospitality Management, Gastronomy and Food systems, Food & Beverage experiences. Extending beyond operational management, this role is meant to encompass a potential progression for contributing to institutional governance, policy development and strategic planning of its academic mission. Key roles & Responsibilities In carrying out his or her role, the “Head of Academic Division” is meant to contribute to teaching, ensure academic leadership and mentoring, oversee implementation of best academic administration practices in compliance with quality assurance requirements. Serving as a member of the senior administrative team, expectations withing these domains are as follows: Academic leadership Provide leadership in support of institutional philosophy, principles, and governance and for the deployment of strategic academic initiatives. Provide strategic insight into advances and emerging trends in the areas of Hospitality, Gastronomy & Food systems, Food & Beverage experiences, Food product development and innovation. Optimise differentiation and respect of curricular intentions, teaching goals, learning and assessment objectives, overseeing design and implementation of comprehensive teaching and assessment frameworks. Ensure content breadth, depth, logical progression and coherence within and across programmes. Work closely with the “Head of Management Education and Career Integration” for optimizing applied learning opportunities and programme content alignment with evolving work-place expectations. Administrative Excellence Manage academic resources efficiently reviewing quarterly teaching assignments to ensure effective programme delivery while respecting equity principles. In collaboration with the Institution senior administrative team, review quarterly teaching assessment reports and provide mentoring in support of faculty development and advancing contemporary teaching practices.  Counsel and guide the “Head of student trajectory and academic operations” to enhance student-centered teaching and learning approaches. Optimize alignment between programme descriptions, marketing materials, and actual delivery and enhance academic documentation to improve clarity and effectiveness. Work in close collaborations with the Administrative and Finance Director for academic budget framework (student recruitment targets; planning of programme offering and teaching assignments). Working in close collaboration with the Comité de direction, carry out data analysis and prepare detailed reports with actionable recommendations for optimizing programme delivery and revenue generating initiatives. Navigate effectively between senior academic administration, academic functions, and operational support ensuring transparent communication regarding project status, challenges, and solutions. Academic Quality Assurance Lead the implementation and maintenance of academic quality assurance principles including transparency, equity, trackability, and accountability to ensure compliance with qualification standards. Monitor key performance indicators for academic excellence. Lead and oversee the academic cycle and Academic Council activities according to approved Terms of References. Required Qualifications Education & Experience Master's degree (PhD preferred) in the areas of “Gastronomy”, “Food Product Development”, “Food Sciences”, “Hospitality Management”, “Food & Beverage Management” or equivalent. Minimum 5+ years regular teaching responsibilities (including in higher education). Leadership & Management Skills Experience in academic affairs management/coordination experience in programme management, student affairs or academic operations management. Proven leadership ability with experience managing academic teams and working effectively with senior management. Track record of successful project management and delivery. Technical Skills & Competencies Experience with academic quality assurance processes including academic documentation, accreditation processes and compliance requirements. Experience in programme mapping, assessment design and data analysis across all levels of academic programmes and training. Proficiency in the use of educational technology and learning management systems and evolving best practices. Essential Personnal and Professional Attributes Language proficiency: Excellent written and verbal communication skills in English; Minimum of Intermediate verbal proficiency in French language (professional working level). Willingness to relocate to Paris. France for full-time on-site presence. Rigorous Standards: Unwavering commitment to academic excellence and process adherence. Integrity & Transparency: Commitment to honest communication about capabilities, challenges, and progress. Adaptability: Willingness to learn and improve based on feedback and changing requirements. Collaborative Leadership: Ability to work effectively across departments and with diverse stakeholders. Capacity to accept and implement senior management decisions constructively. An understanding of the European competency certification framework and of French education system and regulatory bodies will be an asset. Compensation & Benefits Competitive salary: Commensurate with experience, plus comprehensive benefits package including professional development. Application, required documents and selection process: Required documents are: Cover letter outlining career expectations and relevant experience. Comprehensive CV/Resume. Three professional references. Teaching evaluations or portfolio (if available). Application Deadline: 20th May 2026 Start Date: 1st September 2026 or earlier. The selection process includes the following: Initial application review and selection of a short list of candidates. Video or in-person interview with hiring committee of short-listed candidates. Triage for selection of final short list of candidates of interest. Second task-oriented interview with senior institutional management. LCB is an equal opportunity employer committed to diversity and inclusion in education. We encourage applications from qualified candidates of all backgrounds.

Job Description SCHOOL OF CREATIVE ARTS ACADEMY OF VISUAL ARTS Part-time Lecturer (25260664) The Academy of Visual Arts is the first-of-its-kind local institution in delivering Arts and Technology education, which is looking for Part-time Lecturer(s) to teach the course(s) at undergraduate level in the following areas: Unreal VR technology Digital media and fabrication Creative computation Any other related disciplines The ideal candidate should hold a master’s degree or above in related disciplines, with 3 years of experience in creative industries with aspects of content development, budgeting, licensing, administration, marketing, or audience engagement, preferably with teaching experience at the university level. A good command of English and Chinese, excellent communication skills, and a strong sense of responsibility are required. Duties may also include supervision of students, marking of examination papers, and any other work as assigned by the Programme Director and Director of the Academy. The initial appointment will be offered on a part-time basis commencing Sep 2026. Re-appointment thereafter will be subject to performance review and course availability. Salary will be commensurate with qualifications and experience. Application Procedure: Applicants are invited to submit their applications at the HKBU e-Recruitment System. Those who are not invited for an interview 8 weeks after submission of the application may consider their applications unsuccessful. Details of the University’s Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy. The University reserves the right not to make an appointment for the post advertised, and the appointment will be made according to the terms and conditions applicable at the time of offer. Review of applications is ongoing until the position is filled.

Semiconductor fabrication is one of the most complex and precision-driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial-and-error fabrication towards predictive and data-driven manufacturing. We are seeking a highly motivated Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in the group of Dr Yasir Noori. In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post-fabrication device characteristics from process parameters. You will engage with complex, high-dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process conditions. The models you develop will not remain confined to the research lab, but will be validated experimentally and tested at an industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation. The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. You will also be involved in supervising PhD students and junior researchers and play a central role in shaping the research direction of the team. Your work is also expected to contribute to the development of innovative technologies with a clear pathway to commercialisation through the spinout company Deep Fabrication, to influence how semiconductor manufacturing is approached in practice. The role will provide you with deep exposure to nanofabrication processes, experience working with industry-relevant datasets and problems, and the opportunity to publish in leading journals and conferences. It is particularly well-suited to candidates who are motivated by applying machine learning to real-world systems where the underlying physics is complex and not fully understood. This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months. To apply, please submit your CV and a cover letter outlining how your experience and interests align with the aims of the project, and provide responses to the short-listing questions.