Paris Diderot University – Paris 7

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.

Position Overview: Mohammed VI Polytechnic University (UM6P) invites applications for a highly qualified Postdoctoral Researcher to join our research initiative focusing on the integration of Artificial Intelligence (AI) in Chemical Process Systems Engineering (CPSE). The position addresses key challenges in process intensification, sustainability, and advanced process control, aiming to develop AI-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart manufacturing systems, aligned with Industry 4.0 paradigms, and target applications in sustainable energy production, green chemistry, circular economy, and carbon capture, utilization and storage (CCUS). Scientific Challenges Addressed in the Position: Non-linear and dynamic system modeling of chemical processes involving complex thermodynamics and transport phenomena. Optimization under uncertainty and robust decision-making for process design and operational strategies. Integration of first-principles (mechanistic) models with data-driven models (hybrid modeling) for improved accuracy and generalization. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data streams for reliable AI model training. Design of digital twins for process monitoring, fault diagnosis, and predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical processes. Construct multi-objective optimization frameworks (Pareto optimization) to evaluate trade-offs among economic, energy efficiency, and environmental performance metrics. Utilize reinforcement learning (RL) and deep reinforcement learning (DRL) for autonomous process management, dynamic resource distribution, and real-time decision-making. Design and deploy digital twins for integrated chemical processes for virtual commissioning and optimization. Examine extensive heterogeneous data sets (including historical process data, sensor data, and laboratory findings) using advanced AI approaches such as unsupervised learning, transfer learning, and anomaly detection. Collaborate with process engineers and experimentalists to validate models, demonstrate them at pilot scales, and transfer technology to industry partners. Share research results through peer-reviewed publications, conference talks, and patent filings when relevant. Oversee graduate students and support capacity building in AI for CPSE at UM6P. Required Qualifications: Ph.D. in Chemical Engineering, Process Systems Engineering, Artificial Intelligence, Data Science, or a related discipline. Proven experience in process modeling (steady-state and dynamic) using simulation software such as Aspen Plus, gPROMS, or COMSOL Multiphysics. Solid understanding of process control strategies, including model predictive control (MPC), nonlinear control, and optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience in data-driven modeling, deep learning, and reinforcement learning algorithms applied to chemical processes. Track record of scientific publications in peer-reviewed journals in AI, chemical engineering, or process systems engineering. Excellent communication skills and ability to collaborate in multidisciplinary and international teams. Application Process: Candidates are invited to submit the following documents (compiled in a single PDF): A cover letter outlining their research vision, motivation, and relevant experiences. A comprehensive Curriculum Vitae (CV) including a list of publications and any patents. Contact details of two academic referees. 

Job Description As an element of our scientific research activities at the CBS department, we are conducting innovative research on the development of advanced materials for biomass conversion processes. This project focuses on designing and optimizing novel materials that enhance the efficiency and effectiveness of converting biomass into valuable products such as biofuels and fine chemicals. We are seeking a highly motivated candidate to contribute to the exploration of new materials and their applications in thermochemical and biochemical conversion processes. Main Responsibilities: Design, synthesize, and characterize advanced materials for biomass conversion. Conduct experiments to evaluate the performance of these materials in various conversion processes. Investigate the mechanisms of biomass conversion and the role of materials in enhancing reaction efficiency.  Analyze products and materials using advanced characterization techniques (e.g., HPLC, GC-MS, SEM, TEM, BET …). Write scientific reports, and research articles, and participate in conferences. Candidate Profile PhD degree in Materials Science, Chemical Engineering, or a related field. Strong background in materials synthesis and characterization. Experience with biomass conversion processes and catalytic systems is highly desirable. Familiarity with advanced analytical techniques will be valued. Excellent communication skills (oral and written) in English. Good publication track record in peer-reviewed journals. Ability to work independently and as part of a multidisciplinary team. Experience in co-supervision and coordination of graduate students is a plus. Candidature Submission: Applicants should submit: Cover letter outlining research experience, achievements, and research plan. Curriculum Vitae. List of publications. Name and contact information of at least two referees.