Obihiro University of Agriculture and Veterinary Medicine

Description The Water Research Center (WRC) at NYU Abu Dhabi invites applications for a Postdoctoral Associate to contribute to cutting-edge research on membrane fouling and functional membranes, including electrically conductive membranes. The successful candidate will work under the supervision of Professor Raed Hashaikeh in the Mechanical Engineering Department, collaborating with a multidisciplinary team of researchers and graduate students. The postdoctoral associate will have access to state-of-the-art research facilities at NYUAD. This position supports two interconnected research streams: Investigating membrane fouling mechanisms and mitigation strategies in desalination and water treatment processes. Developing and optimizing functional membranes, including electrically conductive membranes, for use in desalination, energy generation, and electrochemical separations. Responsibilities: Conduct research on membrane fouling (biofouling, scaling, organic fouling), diagnostics, and control strategies. Design, fabricate, and characterize functional membranes (e.g., conductive, anti-fouling, electrochemical systems). Operate and evaluate membrane systems (RO, NF, UF) at lab and pilot scales. Apply advanced characterization techniques (e.g., SEM, AFM, FTIR, XRD, TGA, BET, EDX, EIS, CV). Collaborate with interdisciplinary researchers in materials science, chemical and environmental engineering. Contribute to mentorship of students and lab group discussions. Minimum Qualifications: PhD in Chemical Engineering, Environmental Engineering, Materials Science, Mechanical Engineering, or a related field. Strong background in membrane science with demonstrated expertise in either membrane fouling or electrochemical membrane technologies. Experience with membrane fabrication, testing, and characterization. Solid analytical and problem-solving skills; ability to work independently and collaboratively. A strong publication record with first-author articles. Desired Qualifications: Expertise in fouling analysis and mitigation, including cleaning protocols and pretreatment strategies. Familiarity with conductive membranes, carbon-based materials, or mixed-matrix systems. Experience with electrochemical methods (e.g., EIS, CV) and surface functionalization. Knowledge of materials for charge transport and ion exchange. Prior collaboration with international or multidisciplinary research teams. This position will begin in March 2026, as a two-year contract, renewable depending on performance. The terms of employment are very competitive and include housing. Applications will be accepted immediately and candidates will be considered until the position is filled. To be considered, all applicants must submit a cover letter, curriculum vitae, a one-page summary of research accomplishments and interests, a transcript, and at least 2 references, all in PDF format. Applications will be reviewed immediately and the position will remain open until filled. About NYUAD: NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities. EOE/AA/Minorities/Females/Vet/Disabled/SexualOrientation/Gender Identity Employer UAE Nationals are encouraged to apply. Equal Employment Opportunity Statement For people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdpr NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. All interested persons are encouraged to apply for vacant positions at all levels.

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.

Institution: Mohammed VI Polytechnic University (UM6P) Department: Chemical and Biochemical Sciences - Green Process Engineering (CBS) Location: Benguerir/Jorf Lasfar, Morocco Duration: 12 months, renewable based on performance and funding. About Mohammed VI Polytechnic University Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa, striving to position itself among world-renowned universities. Located in the heart of the Green City of Benguerir, UM6P aims to make a significant national, continental, and global impact by driving economic and human development through cutting-edge research and innovation. The Chemical and Biochemical Sciences - Green Process Engineering (CBS) department focuses on advancing sustainable materials and technologies to address modern environmental and agricultural challenges. Position Description The post-doctoral researcher will contribute to projects focusing on the development and characterization of green and sustainable materials for advanced agronomic applications. These materials may include polymers, composites, or hybrid systems aimed at controlled-release fertilizers, biodegradable films, or environmentally friendly coatings. Key Responsibilities Research and Development: Design, synthesize, and characterize green or ecological materials for agronomic applications. Material Optimization: Develop functional materials, such as controlled-release fertilizers, films, or hydrogels, tailored to specific agricultural challenges. Characterization: Conduct structural (FTIR, XRD), thermal (TGA, DSC), morphological (SEM, AFM), and mechanical testing to evaluate material properties. Performance Evaluation: Implement agronomic trials to assess material performance under simulated or real environmental conditions. Scientific Communication: Prepare manuscripts for publication in high-impact journals and present findings at scientific conferences. Collaboration: Engage with interdisciplinary teams within UM6P and external partners to support collaborative research efforts. Mentorship: Co-supervise and mentor Master's and Ph.D. students, contributing to their academic and professional development. Qualifications and Experience Required: Ph.D. in Materials Science, Chemistry, Polymer Engineering, or a related field. Experience in the development of sustainable or green materials for advanced applications. Strong expertise in material synthesis and characterization techniques (e.g., FTIR, XRD, SEM, TGA, BET). Proven experience in data analysis and material performance optimization. Track record of publications in peer-reviewed journals. Preferred: Knowledge of agronomic applications such as controlled-release systems, biodegradable materials, or eco-friendly coatings. Experience with environmental or agricultural material testing. Skills and Attributes: Ability to work independently and collaboratively within a multidisciplinary team. Strong analytical, organizational, and scientific writing skills. Innovative mindset with a focus on developing sustainable solutions. Application Requirements Applicants are invited to submit the following documents: Cover letter detailing research interests, experience, and suitability for the position. Detailed curriculum vitae (CV). List of publications and patents. Contact information for two professional references. Benefits Opportunity to work within a dynamic and innovative research environment at UM6P. Access to state-of-the-art facilities for material synthesis, characterization, and testing. Collaboration with national and international research networks. Competitive salary and benefits package. This position is ideal for candidates passionate about sustainable materials and seeking to make a significant contribution to agronomic and environmental advancements.