Istanbul Okan University

About UM6P: Located in the heart of the Kingdom's capital at Technopolis, University Mohammed VI Polytechnic (UM6P), is an internationally-oriented higher education institution, which is committed to an educational system based on the highest standards of teaching and research in fields crucial to the sustainable economic development of Morocco and Africa. About AI Movement: Ai movement, the International centre for Artificial Intelligence in Morocco, attached to UM6P, is a center of excellence in Artificial Intelligence, which aims to foster the emergence of Moroccan and African know-how in Artificial Intelligence and Data Science. It is UNESCO’s reference center for artificial intelligence in Africa. Ai movement’s goal is to develop a trustworthy, ethical, responsible and inclusive AI on the African continent. Profile: Post-doctoral fellow in Artificial Intelligence - Large Language Models and BDI Systems Ai movement is recruiting a post-doctoral fellow in Artificial Intelligence - Large Language Models and BDI Systems. He/she will work under the supervision of the center's executive president. Your main tasks: Design, develop and apply NLP algorithms in various projects. Contribute to the supervision of doctoral students and interns at the center. Participate in the training courses organized by the center. Work on collaborative projects with companies and external organizations. Interact with researchers and engineers from the center and its ecosystem. Contribute to the influence of Ai movement's research activities by publishing scientific papers on the center's themes. Participate in administrative and management tasks related to research and teaching. Technical skills required: In-depth technical knowledge in AI and Data Science. Proven expertise in research and development in the following areas of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch). Excellent command of IT tools and perfect command of AI and Data Science tools. Profile required: PhD in computer science, specializing in AI and Data Science. Taste for working in a collaborative environment. Good interpersonal skills, rigor, autonomy, organization and writing skills. Strong analytical and synthesis skills. Sense of public interest. Ability to work on several projects as required. Ability to work as part of a team, very good oral and written communication skills (in French and English). Contract: Fixed-term contract, duration: one year

Job description: Sustainable Materials Research Center (SUSMAT-RC) is seeking a highly motivated and talented postdoctoral fellow to join our research team focused on the development of innovative and sustainable sensor systems for industrial applications. This position offers an exciting opportunity to contribute to cutting-edge research in the field of sustainable materials to develop promising solutions that support and promote smart and sustainable practices in industrial processes. The successful candidate will develop innovative and efficient sensing platforms using advanced manufacturing techniques and strategies. The sensor platforms will leverage miniaturized, low-cost, and efficient components, utilizing state-of-the-art manufacturing techniques to enable seamless integration into industrial equipment and infrastructure. Key duties: The successful candidate is expected to: Have strong experience in polymers, molecularly imprinted polymers, and polymer-based nanocomposite synthesis, characterization, and application. Demonstrate knowledge and/or experience in sensor processing including design, fabrication, and characterization. Conduct research to design, develop, and optimize sensors for industrial applications. This includes exploring novel sensing technologies, materials, and data analysis techniques. Conduct rigorous field testing of developed sensors to ensure sensor accuracy, reliability, and robustness. Collaborate with interdisciplinary teams, including engineers, and data scientists, to integrate sensor technologies into comprehensive industrial systems.  Maintain detailed records of experimental procedures, results, and observations.  Prepare technical reports, research papers, and presentations for conferences and scientific publications. Contribute to the generation of new research ideas, participate in grant writing activities, and seek external funding opportunities to support ongoing research projects. Have a good knowledge of analytical techniques (GC/LC-MS, HPLC, etc), spectroscopic techniques (UV-Vis, FTIR, Raman, NMR, XRD, spectrophotometry, etc), and microscopy techniques (SEM, TEM, AFM, etc) necessary for structural and physical-chemical characterization of polymer and polymer-based nanocomposite. Proven experience and familiarity with electrochemical techniques necessary for preparation, characterization and application of polymer and polymer-based nanocomposite: CV, LSV, DPV, SWV, EIS, etc. Broad experience in surface modification of inorganic or organic substrates. Contribute to the group’s activities in the processing of sustainable materials, (bio)polymer composites and their applications as sensing platforms in related fields. Experience in supervising undergraduate, master's and doctoral students. Criteria of the candidate To be considered for this role, you will ideally have: Ph.D degree in analytical chemistry, polymer science, materials science and engineering or any related field. Strong motivation and passion for research in the field of sensors Strong initiative, self-motivated, and focused on achieving quality outputs. Good time management and task prioritization skills. Strong analytical, problem solving and teamwork skills. Ability to work both as part of a team and to work independently. Excellent oral and written communication skills in English. Application and Selection Application folder must contain: Detailed CV. Motivation letter, emphasizing your specific interest and motivation A brief research statement. Contact information of 2 referees. About Mohammed VI About Mohammed 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 Sustainable Materials Research Center (SusMat - RC): The Sustainable Materials Research Center (SusMat - RC) is a multidisciplinary research center engaged in the development of advanced biobased products and materials thereof using biotechnological, physical and chemical conversion strategies and engineering processes (b) understanding and controlling their underlying specific functionalities and the structure-properties relationship acting over multiple length scales from the molecular, nano to the macro level and their cross-interactions and (c) utilizing the gathered knowledge and the invented materials to address key technological challenges in Morocco and African continent and assist the transition toward a green and circular economy.

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.