Banasthali University

Expected Start Date: October 2025 Job Description We are seeking a highly motivated postdoctoral researcher with expertise in vanadium and rare-earth element (REE) recovery, thermodynamic modeling, and materials characterization to join a cutting-edge research team. The project focuses on developing sustainable and optimized processes for vanadium extraction and purification, with the goal of enabling large-scale redox flow battery deployment for energy storage. Scope of Work The successful candidate will work on: Designing and optimizing extraction processes for vanadium from primary and secondary sources (e.g., slags and industrial wastes). Developing purification pathways to produce battery-grade vanadium for energy storage applications. Applying thermodynamic modeling tools (CALPHAD, FactSage) to simulate vanadium phase behavior and guide process improvements. Performing advanced characterization of vanadium-bearing materials to understand their transformation and recovery mechanisms. Evaluating environmental sustainability via Life Cycle Assessment (LCA) and techno-economic analysis. Contributing to the integration of vanadium flows within a circular economy model for battery materials. Key Responsibilities Development and optimization of hydrometallurgical and pyrometallurgical vanadium recovery strategies. Advanced thermodynamic simulation using CALPHAD-based methods, including phase equilibrium modeling in multi-component systems. Comprehensive use of characterization tools, including: SEM, TEM, HAADF-STEM, XRD, and EDS for micro- and nano-scale phase analysis. LA-ICP-MS for ultra-trace elemental quantification. Investigation of impurity phase removal (e.g., Fe, P, Si, REEs). Environmental and economic evaluation of processes via LCA and techno-economic models. Preparation of high-impact publications and contribution to collaborative proposals. Support of graduate student mentorship and lab coordination. Required Qualifications & Experience PhD in Materials Chemistry, Extractive Metallurgy, or Chemical Engineering with a focus on resource recovery or battery materials. Research experience in metal recovery from slags or mineral concentrates, including both experimental and computational methodologies. Proven proficiency in thermodynamic modeling using FactSage, CALPHAD, and complementary simulation platforms like Aspen Plus. In-depth knowledge of electron microscopy, spectroscopy, and structural analysis methods for complex materials. Familiarity with REE and vanadium chemistry, oxidation states, and redox equilibria. Desirable Competences Prior research on critical metals (e.g., V, Ce, La, Nd) in slag systems. Simulation of monazite and rare-earth phosphate precipitation from multi-component melts. Strong background in slag thermochemistry, particularly oxygen fugacity control and partition coefficient modeling. Experience in integrating modeling with leaching and separation processes for scale-up. Advanced data interpretation using electron diffraction, dendritic growth modeling, and REE behavior in Fe-rich matrices. Competences & Personal Qualities Excellent English communication skills (oral and written). Strong publication record in peer-reviewed journals. Capable of both independent and collaborative interdisciplinary research. Demonstrated ability to lead sub-projects, mentor students, and contribute to scientific dissemination.

Position Overview: We are seeking an outstanding Postdoctoral Researcher in Artificial Intelligence (AI) and Data Science with expertise in multi-omics data integration for health and precision medicine. The successful candidate will join a multidisciplinary team developing AI-driven approaches to integrate and analyze genomics, transcriptomics, proteomics, metabolomics, and microbiome datasets to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data fusion, enabling next-generation solutions in precision health and biomedical research. Scientific Challenges Addressed in the Position: Heterogeneity and high dimensionality of multi-omics data requiring advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease progression modeling. Explainability and interpretability of AI models to support clinical decision-making and regulatory compliance in healthcare settings. Scalability and computational efficiency in processing and integrating massive multi-omics datasets from clinical cohorts. Key Responsibilities: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets and clinical metadata. Conduct network-based analysis (gene regulatory networks, protein-protein interaction networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with biologists, clinicians, and bioinformaticians for data interpretation and validation of computational findings in clinical or experimental settings. Disseminate research outcomes through publications in high-impact journals, conference presentations, and workshops. Mentor and support the training of graduate students and early-career researchers in AI and multi-omics integration. Required Qualifications: Ph.D. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or a related field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference, causal modeling, and graph-based AI approaches. Experience in multi-modal data fusion, representation learning, and heterogeneous data integration. Strong publication record in relevant peer-reviewed journals. Excellent communication skills and ability to work in a multidisciplinary environment. Familiarity with cloud-based computing platforms (AWS, Azure, Google Cloud) and high-performance computing (HPC) environments. Understanding of data privacy, security, and ethical considerations in handling clinical data. Application Process: Interested candidates should submit the following documents in a single PDF: A cover letter outlining their research interests, motivation, and relevant experience. A detailed Curriculum Vitae (CV) with a list of publications. Contact details of two academic referees.

About Mohammed VI Polytechnic University (UM6P) Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and innovation. On a specific focus on Africa, UM6P aims to position these fields as the forefront and become a university of international standing. More than just a traditional academic institution, UM6P is a platform for experimentation and a pool of opportunities, for students, professors, and staff. It offers a high-quality living and study environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in order to address real challenges. All our programs run as start-ups and can be self-organized when they reach a critical mass. Thus, academic liberty is promoted as far as funding is developed by research teams. The research programs are integrated from long-term research to short-term applications in linkage with incubation and start-up ecosystems. About the Chemical & Biochemical Sciences Green Process Engineering (CBS) The Chemical & Biochemical Sciences Green Process Engineering Department (CBS) is a component of the Mohammed VI Polytechnic University (UM6P). The main objective of CBS is to set up a distinctive research-teaching program, of international level, to meet the research and teaching challenges of UM6P, 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 to propose new original alternatives in terms of efficiency, environmental friendliness, and sustainability. Job Description The CBS department at UM6P is seeking a highly motivated and qualified researcher for a Postdoctoral position in Hydrometallurgy with the common objective to develop sustainable recycling processes for separation and recovery of valuable and critical metals from various waste streams. The successful candidate will contribute significantly to advancing the department's research initiatives and supporting mission of research excellence and innovation. The work includes the design and experimental study of basic model as well as more applied systems. Key Responsibilities: Expertise in hydrometallurgy, and/or flotation, precipitation, etc. Strong academic credentials and scientific proficiency documented in publications. Strong laboratory/instrumental skills An analytical approach Awareness of diversity and equal treatment issues with a particular focus on gender equality Analyse experimental data, interpret results, and contribute to the development of new hypotheses and research directions.​ Prepare manuscripts for publication in peer-reviewed scientific journals and present research findings at national and international conferences.​ Collaborate with interdisciplinary research teams within the department and with external partners to enhance research outcomes.​ Candidate Criteria A doctoral degree or an equivalent foreign degree in chemistry, chemical engineering or hydrometallurgy. Research Excellence: A strong track record of high-quality research demonstrated through publications in reputable international journals. Collaborative Mindset: Proven ability to collaborate effectively with interdisciplinary teams and external partners to address complex challenges. Communication Skills: Excellent communication and presentation skills in English. Proficiency in French a plus. Entrepreneurial Spirit: Alignment with UM6P's focus on entrepreneurship, Application and Selection: The application folder must contain: Detailed CV, Cover letter along with a comprehensive presentation of the candidate background, research work, projects, and key activities (publications and achievements) Research and teaching statement, entrepreneurial ideas, and concepts if any, and services to UM6P community: max 3-4 pages, 2 reference letters. Our Offer Very competitive salary and benefits package. A unique set of research partners and collaborators.