London Business School Dubai

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.

About UM6P: Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields. The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa. Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally. About the Chemical & Biochemical Sciences Green Process Engineering (CBS) The CBS Department is an entity of the UM6P. The main objective of CBS is to set up a distinctive research-teaching program of international level, in order to meet the research and teaching challenges of UM6P, in particular on green and environmental chemistry applied to all aspects of chemical sciences: organic and inorganic chemistry, analytical chemistry, chemical biology, biochemical and thermal reactions. Research at CBS is organized around several major areas, which aim to answer challenging industrial questions, from complex chemical and biochemical reactions to scale-up and validation of process engineering. CBS projects aim at an in-depth understanding of the molecular mechanisms of all transformations in order to propose new original alternatives in terms of efficiency, environmental friendliness and sustainability. Job Description The CBS is seeking a highly motivated and skilled Postdoctoral Fellow to join a multidisciplinary research team focused on the identification and valorization of Moroccan phytoplankton, with a particular emphasis on algal biomass. The successful candidate will be responsible for the extraction, fractionation, and isolation of bioactive compounds from various algal species. They will apply advanced spectroscopic and analytical techniques to characterize the chemical structure of these metabolites and evaluate their biological potential. The overarching goal is to explore and promote the use of these compounds in pharmaceutical, cosmetic, and agri-food applications. Key Responsibilities: Conduct extraction and fractionation of bioactive metabolites from algal biomass. Isolate and purify target compounds using chromatographic techniques. Perform structural elucidation using advanced spectral and analytical tools (e.g., LC-MS/MS, GC-MS/MS, NMR). Evaluate biological activities such as antioxidant, antimicrobial, anti-inflammatory, etc. Contribute to the development of value-added products based on algal metabolites. Prepare scientific reports, publications, and presentations related to the project outcomes. Support the development of sustainable and scalable valorization strategies for Moroccan phytoplankton resources. Candidate Criteria Ph.D. in Phytochemistry, Natural Products Chemistry, or a closely related field. Solid experience in natural product extraction, purification, and metabolite identification. Proficiency in the use of LC-MS/MS, GC-MS/MS, NMR, and relevant data analysis software (e.g., MestReNova, Thermo scientific, Shimadzu, MS-FINDER, and MZmine, platforms). Familiarity with bioactivity assays and screening protocols is an asset. Strong analytical skills and the ability to work independently and within a team. Excellent scientific writing and communication skills. A good track record of publications in peer-reviewed journals Application and Selection The application folder must contain: Detailed CV, Cover letter along with a synthetic presentation of the background, research works, projects and activities, publications, key achievements, 3 reference letters.

Ref.: 534428 Work type: Full-time Department: Department of Obstetrics and Gynaecology, School of Clinical Medicine (20900) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointment as Post-doctoral Fellow in the Department of Obstetrics and Gynaecology, School of Clinical Medicine (Ref.: 534428) (to commence as soon as possible for two years, with the possibility of renewal subject to funding availability and satisfactory performance) Applicants should possess a doctoral degree (Ph.D.) in Stem Cell Biology, Molecular Biology, Developmental Biology, or a related discipline, with substantial experience in cell culture, molecular techniques, and animal handling. Preference will be given to candidates with expertise in the study of reproductive biology and pluripotent stem cells differentiation, especially those specializing in germ cell development. The appointee is expected to demonstrate both independent research capabilities and effective team collaboration; manage on-going research projects on ovarian reconstitution; publish original research articles in high-impact journals; and secure external competitive grants. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date C.V., with the contact information of at least 3 referees. Applications will be reviewed on a rolling basis, and interviews will commence immediately upon receipt of suitable applications. The position will remain open until filled.