ADIA Lab - Senior Computational Scientist - Material Sciences

Requirements

• Establish and lead a new Material Sciences research group within ADIA Lab, focusing on computational modeling, simulation, and data-driven materials discovery.
• Drive cross-disciplinary research combining physics-based modeling, data science, and high-performance computing to address grand challenges in materials for energy, climate, and technology.
• Develop and apply computational frameworks for multiscale modeling, quantum-to-continuum simulations, and AI-assisted materials design.
• Build and coordinate a strong, collaborative research team and network, with an emphasis on strategic leadership and partnership development.
• Collaborate with national and international research institutions, fostering a community science approach to accelerate progress in computational materials research.
• Build partnerships with universities, laboratories, and industry to initiate joint programs, funding proposals, and collaborative platforms for materials informatics and open data.
• Publish research results in high-impact journals, contribute to white papers and global assessments, and represent ADIA Lab at leading international conferences.
• Supervise and mentor junior scientists, postdoctoral fellows, and students, nurturing future leaders in computational materials science.
• Play a central role in shaping, growing, and coordinating the Materials Science group as a core pillar within ADIA Lab’s mission, ensuring long-term sustainability and global visibility.
• Work closely with ADIA Lab Fellows and the Advisory Board to shape the strategic direction of materials science research at ADIA Lab.
• Basic Requirements
• PhD in materials science, physics, chemistry, computational science, applied mathematics, or a related field.
• Minimum of 6 years of post-PhD experience in computational materials research, data-driven materials design, or closely related domains.
• Preferred Qualifications
• Proven record of leading a research group or major project in computational or data-driven materials science.
• Demonstrated expertise in atomistic and first-principles simulations (DFT, molecular dynamics), mesoscale modeling, or continuum methods.