Universidad Colegio Mayor De Cundinamarca

The position is primarily focussed on the delivery of courses within the school’s undergraduate and postgraduate nursing or midwifery programs. You will work with guidance and direction from course coordinators. The role of tutor utilises contemporary professional/discipline-related knowledge in their teaching practice and carries out activities to develop scholarly expertise. Core Responsibilities: Under direction conduct learning activities including lectures, tutorials, assessment and provide timely feedback to students. Use a variety of pedagogical techniques (including face-to-face, online and blended learning) to foster student learning. Undertake high quality teaching as part of the teaching team, including participation in moderation of marking exercises. Incorporate contemporary professional/discipline-based knowledge in teaching activities Act as a student advisor by responding promptly to queries and directing students to appropriate supports/services. Undertake administrative functions connected to courses taught. Actively participate in meetings and committees at the discipline, program and Academic Unit level. Experience Required:  Relevant and recent clinical experience. High level written and oral communication skills including the ability to effectively collaborate with team members, combined with cultural sensitivity. Demonstrated ability to actively engage and educate a student group. Be capable of using the necessary digital technologies required for effective curriculum delivery. High level administrative and organisational skills. Eligibility Requirements: Bachelor of Nursing or Bachelor of Midwifery and relevant postgraduate qualification in a nursing or midwifery specialisation together with current registration, or eligibility for registration, with the Nursing and Midwifery Board of Australia including a current practicing certificate as a Registered Nurse or Midwife. If you have any questions about the process, please contact Dean.SNM@adelaide.edu.au Please note – By registering on the Casual/Sessional Staff Register, it does not guarantee that employment will be offered. If you already hold a casual contract with Adelaide University, please complete the application form to be considered for future opportunities. Applications welcomed from Australian or NZ citizens, Australian permanent residents and those who have the legal right to work in Australia for the term of appointment.

Location Stratford Campus Salary £39,824 to £45,281 per annum inclusive of London Weighting Post Type Part Time Hours per Week 17.5 Post Type Permanent Closing Date Sunday 29 March 2026 Interview Date Friday 17 April 2026 Reference 0377-26-S University of East London Founded in 1898, we at the University of East London (UEL) are proud to change lives through education. Our goal is to advance industry 5.0 careers-first education and provide a clear path to the jobs and opportunities of the future. We’re committed to driving diversity in the 5.0 talent pipeline, working in partnership to promote talent wherever it is found and creating a balanced, inclusive, and green future. But we can't achieve this goal alone. We need forward-thinking, innovative, and curious individuals like you to join our community and help us craft our future. As part of our team, you'll have the opportunity to work with a diverse range of people who share your passion for creating positive change. We’re an inclusive and welcoming community that is constantly moving forward, never satisfied with the status quo. About Us The Neighbourhood Health Hub at UEL’s Stratford campus is an innovative, community-facing health facility that brings together clinical services, education and research under one roof. As part of the School of Health, Sport and Bioscience (HSB) and the Office of the Health Campus, the Hub houses the UEL Exercise Rehabilitation Centre – a pioneering service delivering evidence-based exercise interventions for people living with non-communicable diseases. This is an exciting time to join a growing, multidisciplinary team at the forefront of clinical exercise physiology education and practice in East London. We are committed to widening participation, health equity and building meaningful partnerships with NHS trusts, community organisations and local authorities to improve health outcomes across our diverse communities. About the Job We are seeking a skilled and motivated Clinical Exercise Physiologist to join the UEL Exercise Rehabilitation Centre on a part-time basis (0.5 FTE). Based at the Neighbourhood Health Hub on our Stratford campus, this role combines direct clinical service delivery with student supervision and mentoring. You will deliver person-centred exercise rehabilitation programmes for patients with complex non-communicable diseases, supervise and mentor MSc Clinical Exercise Physiology students on clinical placement, and contribute to the growth and development of this exciting new service. The role also involves supporting clinical governance, developing referral pathways with NHS and community partners, and contributing to teaching within the MSc programme as required. About You You will hold an MSc in Clinical Exercise Physiology (or ICSESP-recognised equivalent) and have experience of exercise testing, prescription and delivery for clinical populations, including patients with multiple comorbidities. You will have a strong understanding of CEP-UK scope of practice and relevant professional guidelines, alongside knowledge of health and safety, risk assessment and clinical governance procedures. You will be an effective communicator with excellent interpersonal skills, able to work sensitively with patients with varying levels of health literacy and complex communication needs. Self-motivated with a proactive approach to service development, you will be comfortable working both independently and as part of a multidisciplinary team. AHCS registration (or ability to obtain within 6 months), experience of supervising students or junior staff, and additional rehabilitation qualifications are desirable. A first aid qualification (including defibrillation) is required, or willingness to complete on appointment. Benefits Package Here at UEL we understand it’s about more than just a salary. It’s about the entire package on offer along with our outstanding work environment and inclusive culture. We know the market rates for this vacancy and will offer a genuinely competitive salary DOE. We care deeply about your wellbeing and our benefits includes: 20% pension (4% from you, 16% from us), 35 days holiday (plus 3 days over Christmas) plus bank holidays, hybrid working for most positions, and we’re always open to consider flexible working arrangements. We have a subsidised gym membership, 3 times base salary life assurance, a cashback healthcare scheme and wide variety of other generous benefits including discounts on: Apple and Samsung products, holidays, clothing, fitness, lifestyle, dining out and weekly shopping. Equal Opportunities As an inclusive, equal-opportunities employer, our Stonewall membership and Athena Swan awards reflect our ongoing dedication to Equality, Diversity and Inclusion and we’re committed to closing the "diversity pay gap". We’re proud of the progress we’ve made and honest that there’s more to do. We’re determined to keep moving forward so everyone at UEL can thrive. So, if you’d like to take your career to the next level with us here at the University of East London and are inspired by our environment and commit to success, we want you to apply today! Please note that this advert may close earlier than the stated deadline if we receive a high volume of applications. We encourage you to submit your application as soon as possible. We are unable to offer visa sponsorship for this position. Applicants must therefore already have the right to work in the UK at the time of application. Further Details Job Description & Person Specification To contact the recruitment team please phone +44 20 8223 3000 x 4600 or e-mail jobs@uel.ac.uk CVs without a completed application form will not be accepted. At UEL we are committed to working together to build a community which values diversity in both our staff and student populations, is representative and inclusive, enabling all to progress and thrive

About Mohammed VI Polytechnic University (UM6P): Located at the heart of the Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, was established to serve Morocco and the African continent and to advance applied research and innovation. This unique university, with state-of-the-art infrastructure, has woven an extensive academic and research network, and its recruitment process is seeking outstanding academics and professionals to promote Morocco and Africa’s innovation ecosystem. About the department Vanguard works on the development of innovative and interdisciplinary applied research projects. From technological innovation to the transfer of research to industry, Vanguard has also the mission of developing an ecosystem of related start-ups. For more information about our Center, please visit our webpage: https://vanguard.um6p.ma/ Offer description: There are many systems of interest to scientists that are composed of individual parts or components linked together in some way. Examples include the Internet, a collection of computers linked by data connections, human societies, which are collections of people linked by acquaintance or social interaction, transportation systems and biological interactions. These systems are represented as networks. A network is a set of objects that are connected to each other in some fashion. Mathematically, a network is represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between the objects. Many real world networks have a multidimensional nature such as networks that contain multiple connections. For instance, transport networks in a country when considering different means of transportation. The train and bus routes are different types of connections and should in some models be represented by different kinds of edges. These kind of situations can be modeled using multilayer networks which emphasize the different kind or levels, known as layers, of connections between the elements of the network and the interactions between these levels as well. In order to capture the structure and complexity of relationships between the nodes of networks with a mul-tidimensional nature, tensors are used to represent these kind of networks. For example, the transport network mentioned earlier would be represented by a 4th order tensor A 2RN_L_N_L  where L is the number of the layers (transportation means) and N is the number of nodes (stations or stops). Using convenient tensor products, the goal is to define measures to analyze different multidimensional networks based on their adjacency tensors. However, collecting all the interactions in the systems and sometimes even observing all the components is a challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non fully observed data. These methods can be applied to complete or predict links in a network. However, missing information in a network can include both missing edges and nodes which makes classical matrix completion method insufficient. However,we may collect other information and features about the elements of the network. Therefore, side information about the nodes along with the observed edges need to be exploited. The problem of network completion arrises also for applications where the network has a multidimensional representation such as multiplexes and multilayer networks. Since multidimensional networks can be represented by tensors, one can think of applying tensor completion methods which have proved to be efficient in many applications such as image and video reconstruction. However, the same issue arises, tensor completion methods can not be directly applied to recover the links of the network giving the fact that the data is sparse most of the time. We aim to use auxiliary information about the multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover missing links in a multilayer network with connected components. An important constraint in network completion is that the factorization must only capture the non zero entries of the tensor. The remaining entries are treated as missing values, not actual zeros as is often the case in sparse tensor and matrix operations. Therefore, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for  parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel algorithms and methodologies for tensor completion in multidimensional networks. This includes exploring optimization techniques, tensor decompositions, and incorporating auxiliary information for more accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing efficient and scalable algorithms that can handle large-scale datasets. Tensor Analysis: Analyze the structure and properties of multidimensional networks represented as tensors. Investigate different measures and metrics for characterizing network connectivity and relationships. Sparse Optimization: Address the challenge of sparse optimization for tensors by integrating randomized algorithms into optimization frameworks. Study the theoretical foundations of randomized algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational efficiency. Investigate parallel algorithms and architectures that can exploit the inherent parallelism in tensor operations. Collaboration: Collaborate with interdisciplinary teams including computer scientists, statisticians, and domain experts to apply tensor completion techniques to real-world applications, especially in the case of social sciences. This involves effective communication and coordination to ensure the successful integration of mathematical methods into practical systems. Publication and Dissemination: Publish research findings in top-tier journals and present results at conferences and workshops. Disseminate knowledge and contribute to the academic community by sharing insights and methodologies developed during the course of the project. Mentorship and Training: Provide mentorship and guidance to graduate students and junior researchers involved in related projects. Share expertise and knowledge in applied mathematics, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra, or equivalent. Prior experience on the subject is highly desired.