Dr Abdulrahman Azab Mohamed | Computational Materials Science | Best Researcher Award

Dr Abdulrahman Azab Mohamed | Computational Materials Science | Best Researcher Award

Dr. Abdulrahman Azab Mohamed is a visionary researcher in High-Performance Computing (HPC) ๐Ÿ”ฌ, Cloud Federation โ˜๏ธ, and Data Infrastructure ๐Ÿ”. He earned his Ph.D. from the University of Stavanger, Norway ๐Ÿ‡ณ๐Ÿ‡ด, and now serves as Senior Advisor at Sigma2 AS, NeIC, and Science Support Specialist at EuroHPC LUMI ๐ŸŒ. With 30+ publications ๐Ÿ“ and leadership in major European digital infrastructure projects ๐Ÿ‡ช๐Ÿ‡บ, he excels in secure data sharing, meta-scheduling, and reproducibility. Proficient in tools like Docker ๐Ÿณ, Kubernetes โ˜ธ๏ธ, and Python ๐Ÿ, Dr. Azab is shaping the future of sustainable, federated scientific computing worldwide ๐Ÿš€.

Dr Abdulrahman Azab Mohamed, University of Oslo & Sigma2/Norwegian Research Infrastructure Services, NRIS, Norway

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๐ŸŽ“ Education

Dr. Abdulrahman Azab Mohamed holds a Ph.D. in Information Technology ๐ŸŽ“ from the University of Stavanger, Norway ๐Ÿ‡ณ๐Ÿ‡ด, where he developed a novel meta-scheduling architecture for Grid and Cloud federated systems. He earned his Masterโ€™s in Automatic Control Systems Engineering โš™๏ธ from Mansoura University, Egypt ๐Ÿ‡ช๐Ÿ‡ฌ, focusing on semantic Grid and automated virtual organizations. His academic journey began with a B.Sc. in Electronics and Computer Systems Engineering ๐Ÿ’ป๐Ÿ“ก, where he graduated top of his class ๐Ÿฅ‡ with a stellar GPA and deep specialization in computational systems. His foundation blends engineering precision, computational theory, and applied infrastructure innovation.

๐Ÿ’ผ Experience

Dr. Abdulrahman Azab Mohamed has over 25 years of multifaceted experience across academic ๐ŸŽ“, research ๐Ÿงช, and industry ๐Ÿ’ผ domains. Currently serving as Senior Advisor at Sigma2 AS ๐Ÿ‡ณ๐Ÿ‡ด, he leads initiatives in HPC ๐Ÿ–ฅ๏ธ, AI cloud โ˜๏ธ, and sensitive data infrastructure ๐Ÿ”. He has held key roles at EuroHPC LUMI ๐ŸŒ, NeIC ๐Ÿš€, and the University of Oslo ๐Ÿซ, managing large-scale digital infrastructure and international collaborations. With hands-on leadership in bioinformatics ๐Ÿ”ฌ, grid/cloud computing โš™๏ธ, and software engineering ๐Ÿ’ป, Dr. Azabโ€™s career spans strategic planning, technical innovation, and e-science advancement on a global scale ๐ŸŒ.

๐Ÿ“˜ Book Chapters

Dr. Abdulrahman Azab Mohamed ๐Ÿ“˜ has significantly enriched the field of distributed computing and HPC through impactful book contributions. He co-edited “Nordic e-Infrastructure Tomorrow ๐Ÿ“ก” (Springer, 2025), presenting pioneering insights from NeIC 2024 on federated e-infrastructure across the Nordics ๐ŸŒ. His book โ€œHiMan ๐Ÿ“Šโ€ introduces a peer-to-peer grid framework revolutionizing computational collaboration. In โ€œSTROLL ๐Ÿ”„โ€, co-authored with Hein Meling in Grid Computing (Intech), he outlines scalable task deployment models for grid systems. These works reflect Dr. Azabโ€™s commitment to decentralized, secure, and efficient computing environments, offering practical frameworks and visionary architectures for the global scientific and research community ๐ŸŒ.

๐Ÿ’ป Technical Skills

Dr. Abdulrahman Azab Mohamed possesses a robust suite of technical skills, bridging software development ๐Ÿ’ป, HPC system design โš™๏ธ, and cloud computing โ˜๏ธ. He is proficient in languages like C++, Java, Python, R, and VB.Net ๐Ÿง , with hands-on experience in distributed systems, MPI/OpenMPI, MapReduce, and gRPC ๐Ÿ”—. His expertise spans HPC schedulers (SLURM, PBS, HTCondor), meta-schedulers (UNICORE), and container technologies like Docker ๐Ÿณ, Kubernetes โ˜ธ๏ธ, and OpenShift. He manages cloud platforms including OpenStack and Azure ๐ŸŒ and excels in data management, semantic interoperability, and parallel code optimization ๐Ÿงฌ. With FitSM certifications and strong leadership ๐Ÿง‘โ€๐Ÿ’ผ, his contributions are both technical and strategic.

๐Ÿ”ฌ Research Focus

Dr. Abdulrahman Azab Mohamedโ€™s research is centered on advancing High-Performance Computing (HPC) ๐Ÿ’ป, Cloud Federation โ˜๏ธ, and Secure Data Infrastructure ๐Ÿ” to enable scalable, efficient, and privacy-aware scientific computing. His expertise spans meta-scheduling algorithms โฑ๏ธ, containerized computing with Docker ๐Ÿณ, and sensitive biomedical data management ๐Ÿงฌ. His contributions include variant calling accuracy in genomics ๐Ÿงซ, cybersecurity in multistage cloud attacks ๐Ÿ”’, and peer-to-peer scheduling in grid systems ๐ŸŒ. Dr. Azabโ€™s interdisciplinary focus integrates AI, distributed systems, and reproducible science ๐Ÿง ๐Ÿ›ฐ๏ธ, empowering data-driven research across domains like bioinformatics, medical diagnostics, and federated cloud platforms ๐ŸŒ.

๐Ÿ“š Publication Top Notes

Accuracy and efficiency of germline variant calling pipelines for human genome data

A finite state hidden markov model for predicting multistage attacks in cloud systems

Enabling docker containers for high-performance and many-task computing

Serum hepcidin levels inย Helicobacter pylori-infected children with iron-deficiency anemia: a caseโ€“control study

An adaptive decentralized scheduling mechanism for peer-to-peer desktop grids

Evaluation and benchmarking of singularity mpi containers on eu research e-infrastructure

A pure peer-to-peer desktop grid framework with efficient fault tolerance

Mr Muhammad Abdul Moiz | Computational Materials Science | Best Researcher Award

Mr Muhammad Abdul Moiz | Computational Materials Science | Best Researcher Award

Mr. Muhammad Abdul Moiz is a dynamic researcher in materials science and engineering ๐Ÿงช, currently pursuing a dual Erasmus Mundus M.Sc. in Functional Advanced Materials Engineering with AI for Sustainability at TU Darmstadt ๐Ÿ‡ฉ๐Ÿ‡ช and Grenoble INP ๐Ÿ‡ซ๐Ÿ‡ท. Graduating Magna Cum Laude ๐Ÿ… from IST Islamabad, he specializes in photocatalysis, solidification modeling, DFT, and sustainable materials ๐ŸŒฑ. With hands-on experience at Saint-Gobain, NESPAK, and BMW, and skills in Python, COMSOL, CASTEP ๐Ÿ’ป, he bridges theory and simulation for advanced applications. His 6+ international publications ๐Ÿ“š reinforce his profile as a future leader in energy-efficient, AI-driven material innovation โšก.

Mr Muhammad Abdul Moiz, TU Darmstadt, Pakistan

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๐ŸŽ“ Educationย 

Mr. Moiz is currently pursuing a dual Erasmus Mundus M.Sc. in Functional Advanced Materials Engineering with AI for Sustainability (FAME-AIS) at TU Darmstadt ๐Ÿ‡ฉ๐Ÿ‡ช and Grenoble INP ๐Ÿ‡ซ๐Ÿ‡ท (2023โ€“2025). His thesis focuses on energy absorption in LPBF lattice cores and phase-field modeling of non-isothermal solidification โš™๏ธ๐Ÿ”ฅ. He achieved top distinction (M1: 16.2/20, M2: A/1.38) ๐Ÿ…. He previously earned a B.Sc. in Materials Science and Engineering from IST Islamabad ๐Ÿ‡ต๐Ÿ‡ฐ, graduating Magna Cum Laude ๐Ÿฅ‡, with a thesis on band structure engineering of ZnO for photocatalysis using DFT and experiments โš›๏ธ๐Ÿ”ฌ.

๐Ÿ’ผ Experienceย 

Mr. Muhammad Abdul Moiz brings diverse research and industry experience across Europe and Asia. As a Research Assistant at TU Darmstadt ๐Ÿ‡ฉ๐Ÿ‡ช, he works on LAMMPS simulations and metadynamics for cementitious materials ๐Ÿงฑ. At Saint-Gobain/SIMaP France ๐Ÿ‡ซ๐Ÿ‡ท, he led FE modeling and sintering studies for refractory ceramics ๐Ÿงช. Previously at NESPAK ๐Ÿ‡ต๐Ÿ‡ฐ, he supervised renewable energy projects โ™ป๏ธ and biogas systems. His industrial internships at BMW ๐Ÿš— and Pakistan Aeronautical Complex โœˆ๏ธ enriched his skills in corrosion analysis, SEM, and mechanical testing. His toolkit includes COMSOL, Python, AutoCAD, and MATLAB, showcasing strong technical depth ๐Ÿ’ป.

๐Ÿ› ๏ธ Technical Skills

Mr. Moiz is equipped with a robust suite of technical proficiencies across simulation platforms including Abaqus CAE, COMSOL Multiphysics, Ansys, MATLAB, and MOOSE ๐Ÿ”ฌ๐Ÿงฎ. He excels in materials design tools like CASTEP and Materials Studio ๐Ÿงฑ๐Ÿง . As a skilled programmer, he works fluently with Python, C++, and Scikit-learn ๐Ÿ‘จโ€๐Ÿ’ปโš™๏ธ. His additional toolkit includes AutoCAD, LaTeX, OriginPro, and Microsoft Office ๐Ÿ“Š๐Ÿ“. Personally, he thrives in structured problem-solving, data documentation, conflict management, and analytical reasoning ๐Ÿง ๐Ÿงฉ. These interdisciplinary strengths position him as a versatile researcher for advanced computational materials and sustainable engineering applications ๐ŸŒโš›๏ธ.

๐Ÿ”ฌ Research Focus

Mr. Muhammad Abdul Moizโ€™s research focuses on computational and experimental optimization of semiconductor materials such as transition metal-doped ZnO for photocatalysis, band gap tuning, and energy applications ๐ŸŒฑโšก. Using DFT+U, ab initio methods, and rapid synthesis techniques, he explores how dopants influence optical and electronic behavior in nanomaterials ๐Ÿงช๐Ÿ’ก. His work enhances visible light activity, improves dye degradation, and supports green technologies through material innovation. With publications in Materials Today Communications and Chemical Physics Letters, his aim is to advance sustainable materials for solar, environmental, and optoelectronic uses ๐Ÿ”‹๐ŸŒ๐Ÿ“ˆ.

๐Ÿ“š Publications

Optimization of Electronic and Optical Properties of Transition Metal Doped ZnO By DFT+U Method and Supported by Experimental Findings
Authors: Muhammad Abdul Moiz
Journal: Materials Today Communications, 2022

Band Gap Engineering of ZnO via Transition Metal Doping: An Ab Initio Study
Authors: Muhammad Abdul Moiz, Abdullah Mumtaz, Muhammad Salman, Syed Wilayat Husain, Abrar H. Baluch, Muhammad Ramzan
Journal: Chemical Physics Letters, 2021

Activating ZnO-Based Hierarchical Particles for Visible Light Dependent Photocatalytic Performance via Cr-Incorporated Rapid Chemical Synthesis
Authors: Muhammad Abdul Moiz
Journal: Crystal Research and Technology, 2021

Enhancement of Dye Degradation by Zinc Oxide via Transition-Metal Doping: A Review
Authors: Muhammad Abdul Moiz, Abdullah Mumtaz, Muhammad Salman, Hifsa Mazhar, Muhammad Abdul Basit, Syed Wilayat Husain, Muhammad Ramzan
Journal: Journal of Electronic Materials, 2021