Jiangxi University of Finance and Economics

Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, is established to serve Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in the metropolitan area of Marrakech. The School of Computer Science at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco, is seeking a postdoctoral candidate in the area of federated learning and wireless communications. The candidate must hold (or about to complete) a PhD in the related fields. The candidate will be involved in a two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication fields. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including non-iidness of data distribution, system heterogeneity, and dynamic environments. Key duties: The Postdoctoral Fellow is expected to: Publish in high-impact journals in the field. Supervise graduate and undergraduate students. Criteria of the candidate: PhD in the field of wireless communication, computer science, or any related field. Strong publication record in high-impact conferences/journals. Aptitude for teamwork, problem-solving, and collaborative relationships Strong technical background in at least one of the following areas: Mathematical optimization, Resource allocation, Machine learning, Wireless communication. Good communication skills in oral and written English Employment terms: The successful candidate will be employed by Mohammed VI Polytechnic University (UM6P) based at Benguerir (50 km north of Marrakech), Morocco. Applications and selection procedure: Applications must be sent using a single electronic zipped folder with the mention of the job title in the mail subject. The folder must contain: A 1-page cover letter. A detailed CV. A research statement. A digital copy of the PhD thesis. Contact information for at least two potential academic referees. The earliest available starting date of the candidate. Shortlisted candidates will be invited for an Interview.

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: Located at the heart of the future Green City of Benguerir, MohammedVI Polytechnic University (UM6P), a higher education institution with an international standard, is established to serve Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high-quality academics and professionals to boost its quality-oriented research environment in the metropolitan area of Marrakech. About MSN department: The Department of Materials Science, Energy, and nano-Engineering (MSN) at Mohammed VI Polytechnic University in Morocco has been in operation for 4 years and during that time expanded quite substantially. The number of employees is around 42, including 13 professors, 24 guest professors, 15 post-Doctoral fellows, 40 PhD students, and 52 Executive Master students. The department is organized in 4 Scientific Areas: Energy (batteries and hydrogen), Polymers and Composite Materials, Surface Science, and Sustainable Materials and Re-cycling. Description of the position and duties: The position is devoted to organic synthesis and studies of ion conducting polymers and polymer-based membranes (anion and cation exchange membranes). These materials will be used in hydrogen fuel cells and for water electrolyzer (alkaline and PEM). The research will focus on developing synthetic strategies and know how to design, to synthesis and functionalize these materials from the molecular level to achieve membranes with high selectivity, conductivity, and stability. The candidate is expected to have an extensive experience in chemistry of polymers and organic synthesis, development of new monomers, ionomers, ionogels, oligomers and polymers along with chemical modification and post- polymerization modification, polymer characterization, preparation of functional membranes, as well as analysis of ion selectivity and conductivity, mechanical and thermal properties with a focus on thermochemical stability and aging, membrane testing to determine IEC, transference number and selectivity, chemical stability and assembling/testing with electrodes. The successful candidate is expected to contribute and establish active research in the field of selective membranes for water electrolysis to produce hydrogen and for fuel cells within the hydrogen group at the MSN department and to participate indifferent activities of the department. The applicant must have a Ph.D. in Chemistry, polymer chemistry. Extensive experience in membrane development, testing and polymer synthesis, functionalization, modification, are mandatory. Knowledge or mastering computational fluid dynamics software and modeling tools to simulate and study fluid flow through porous electrode, membranes, electrode membrane assembly (MEA) for electrolysis and fuel cells will be an asset. Preference will be given to applicants with excellent and relevant R&D along with excellent publication track record. Good communication skills, both verbal and writing are mandatory in English.