Ms. Mouna Sbai Idrissi | Computational Science | Best Researcher Award
Mouna Sbai Idrissi from Hassan II University | Morocco
Mouna Sbai Idrissi is a dedicated researcher and doctoral candidate at Université Hassan II, Faculty of Sciences Ben M’Sick, Casablanca, Morocco, specializing in the study of silica-based glasses using molecular dynamics and artificial intelligence. Her academic and research journey reflects a strong commitment to the intersection of physics, material science, and computational modeling. With an educational background spanning from physics and new technologies to specialized certifications in AI, digitalization, and scientific communication, Mouna has built a multifaceted skill set. She has actively contributed to academia as a tutor, practical course instructor, and co-supervisor for undergraduate and master’s projects, demonstrating her ability to guide and mentor future scientists. Her research has been presented in both national and international conferences, where she has delivered oral and poster communications on advanced topics like machine learning applications in materials science. Mouna’s work has also led to publications in reputable journals, covering structural and mechanical properties of glasses, predictive modeling, and nanomaterial synthesis. Known for her adaptability, teamwork, and innovative thinking, she combines technical expertise with a passion for continuous learning. Beyond her research, she is engaged in academic event organization and community activities, reflecting her commitment to both scientific advancement and academic service.
Professional Profile
Google Scholar
Education
Mouna Sbai Idrissi’s academic trajectory is characterized by consistent advancement in physics, materials science, and computational methods. She is currently in her third year of doctoral studies in Sciences and Techniques at Université Hassan II, focusing on the structural and mechanical properties of silica-based glasses through molecular dynamics and AI. Alongside her doctoral program, she has undertaken numerous certified trainings, including AI applications, digitalization, intellectual property, research methodology, machine learning, advanced Python, SQL, and Power BI. Her exposure to international learning environments includes participation in ICTP’s workshop on Classical and Quantum Machine Learning for Condensed Matter Physics. She holds a Master’s degree in Physics and New Technologies, where her thesis applied deep learning for predicting the Young’s modulus of silicate glasses, and a Bachelor’s degree in Physics and its Applications, with a final project on photovoltaic cell simulation. Her foundational studies include a DEUG in Physics of Matter and multiple baccalaureate-level certifications in physical sciences, humanities, and electrical sciences. This diverse educational portfolio is complemented by seminars on microscopy and scientific publishing, demonstrating her dedication to interdisciplinary knowledge acquisition. Her academic growth reflects a balance between theoretical mastery, experimental techniques, and computational innovation.
Professional Experience
Mouna has accumulated substantial teaching and academic supervision experience at Université Hassan II, where she has served in various pedagogical roles. She has co-supervised undergraduate and master’s final projects, guiding research on hydrogen production modeling and glass phase prediction using AI. As a tutor, she has led sessions in geometric optics, electricity, and thermodynamics, preparing students through problem-solving exercises, past examination reviews, and personalized guidance. Her laboratory teaching responsibilities include practical courses in optics, thermodynamics, electrostatics, modern physics applications, and electrokinetics, covering both undergraduate and master’s levels. Mouna’s contributions extend beyond teaching into academic administration and event organization, where she has been an active member of committees for scientific days, doctoral welcome events, and national research meetings. She has also participated in exam invigilation, student orientation, and conference coordination. Her engagement demonstrates not only her teaching competence but also her organizational and leadership abilities. In addition, she has been involved in para-university activities such as facilitating training programs and contributing to innovation clubs. These experiences have allowed her to integrate pedagogical skills with her research expertise, strengthening her role as both an educator and a scientist committed to fostering academic excellence.
Research Interest
Mouna’s research interests lie at the intersection of materials science, computational modeling, and artificial intelligence. Her doctoral work focuses on studying the structural and mechanical properties of silica-based glasses through molecular dynamics simulations coupled with machine learning algorithms, aiming to develop predictive models for properties such as Young’s modulus and ion mobility. She is particularly interested in understanding how compositional variations, such as the incorporation of TiO₂, affect the mobility of alkali ions in glass matrices. Her broader scientific curiosity extends to nanomaterials, including the synthesis and functionalization of nanoparticles for photocatalytic applications. She also explores the use of deep learning in physical sciences to accelerate discovery, improve predictive accuracy, and optimize material properties. In addition to core research, she is keen on developing computational tools for energy-related applications, such as modeling hydrogen production systems powered by photovoltaic panels. Mouna’s vision is to bridge the gap between experimental materials science and computational intelligence, enabling faster innovation cycles and sustainable technological solutions. She remains open to interdisciplinary collaborations, particularly those that merge condensed matter physics, nanotechnology, and AI to address industrial and environmental challenges.
Research Skills
Mouna possesses a robust set of research skills spanning computational, experimental, and analytical domains. She is proficient in programming languages such as Python, C/C++, and Fortran, with expertise in applying machine learning and deep learning techniques to materials modeling. Her computational toolkit includes MATLAB, MATLAB Simulink, LabVIEW, Origin, and molecular dynamics simulation packages, enabling her to model, simulate, and analyze complex material systems. Experimentally, she has hands-on experience with techniques such as transmission electron microscopy (TEM) and other material characterization methods. She is skilled in designing simulation workflows for predicting structural and mechanical properties of glasses, as well as for renewable energy systems modeling. Her research communication skills are demonstrated through numerous oral and poster presentations at scientific conferences, as well as publications in peer-reviewed journals. Additionally, she is adept at data visualization, statistical analysis, and preparing publication-ready figures and manuscripts. Her proficiency in Word, Excel, PowerPoint, and LaTeX supports her academic writing and reporting activities. Fluent in Arabic, French, and English, Mouna can effectively engage with diverse research communities. Combined with her adaptability, teamwork, and innovative mindset, these skills make her well-equipped to tackle multidisciplinary research challenges.
Awards and Honors
Mouna’s academic dedication has been recognized through prestigious awards and honors. Notably, she is a recipient of the Bourses Doctorants-Moniteurs (PASS – Associate Scholarship PhD), awarded to doctoral candidates who demonstrate both academic excellence and teaching contributions. This recognition underscores her dual role as a researcher and educator, excelling in advancing scientific knowledge while mentoring students. Her research has earned invitations to present at both national and international scientific gatherings, such as the Rencontre Nationale des Jeunes Chercheurs and the International Conference on Research and Innovation. She has also been selected to participate in specialized international workshops, such as the ICTP’s program on Classical and Quantum Machine Learning for Condensed Matter Physics, reflecting her integration into global scientific networks. The breadth of her conference contributions—ranging from oral communications on glass property prediction to poster sessions on molecular dynamics simulations—highlights her commitment to disseminating knowledge. These achievements, combined with her teaching roles and active involvement in academic events, demonstrate her standing as a promising scientist whose work bridges experimental and computational approaches in material science. Her awards serve as a testament to her hard work, innovation, and potential for future research impact.
Publications Top Notes
Title: Synthesis of perfect TiO₂ nanospheres decorated by silver shell nanoparticles for photocatalytic applications
Year: 2024
Citation: 5
Title: Structural and mechanical properties of alkali silicate glasses: Insights from molecular dynamics simulations and artificial intelligence
Year: 2025
Title: Young’s modulus of calcium-alumino-silicate glasses: Insight from machine learning
Year: 2024
Conclusion
Mouna Sbai Idrissi embodies the profile of a modern researcher—technically skilled, pedagogically active, and deeply engaged in scientific advancement. Her journey from undergraduate physics studies to doctoral research has been marked by continuous learning, interdisciplinary exploration, and a drive to apply artificial intelligence in solving complex material science problems. She has effectively balanced her roles as a researcher, educator, and academic organizer, contributing meaningfully to both student development and the broader research community. Through her publications, conference presentations, and teaching activities, she has demonstrated not only technical expertise but also strong communication and leadership abilities. Her ability to merge computational simulations with experimental insights positions her at the forefront of emerging trends in glass science and nanotechnology. Looking ahead, Mouna aspires to expand her research collaborations, advance innovative AI-driven materials design, and contribute to sustainable energy and advanced materials development. Her dedication to knowledge dissemination, coupled with her adaptability and problem-solving mindset, ensures that she will continue to make significant contributions to the scientific community while inspiring future generations of researchers.