Research Scientist II at The University of Texas at Arlington Portal

Job SummaryThe Research Scientist II position will assist with research and development projects that include design execution and publication of results as well as supporting new grant and contract opportunities. The focus of the research is on Multiphysics structural modeling and physics-informed machine learning ( PIML ) for performance prediction and durability assessment of fiber-reinforced composite materials. Role may include contributing to defining the research scope of the Institute leading specific project tasks and contributing to patentable ideas and proposal development. Activities are typically a combination of laboratory experimentation computational modeling data analysis and project coordination under moderate supervision.Essential Duties And ResponsibilitiesConduct integrated research on multiphysics modeling experimental characterization and AI-based prediction of composite materials including: Multiphysics modeling (electrical thermal and mechanical coupling) using COMSOL / MOOSE /FEniCS and Abaqus for predictive performance analysis. Experimental dielectric and mechanical testing (BbDS TSDC DIC MTS ) to evaluate microstructural and interfacial phenomena. Development of physics-informed machine learning frameworks (PINNs neural operators PyTorch NVIDIA Modulus) to accelerate simulation and degradation modeling. Integration of experimental and computational results to establish cross-property correlations and material-state predictors Lead or contribute to the preparation of research proposals technical reports manuscripts and conference presentations. Supervise and mentor graduate and undergraduate students; assist in training on laboratory instrumentation and safety procedures. Participate in IPPM collaborative research assist with equipment setup laboratory commissioning and support industrial and federal R&D projects. Contributing to patentable ideas and proposal development. Document work and maintain accurate research records Present findings/results through oral and written communication to internal and external collaborators. Multi-task and work both independently and collaboratively in interdisciplinary teams. Stay abreast of current technological and scientific advancements in composites AI and materials modeling. Coordinate and collaborate with other research units within UTARI and UTA as needed.Minimum QualificationsPh.D. in Mechanical Engineering Materials Science or a closely related discipline. Two (2) years of experience in materials characterization and computational modeling or an equivalent mix of education and relevant experience in similar role. Demonstrated expertise in finite element modeling ( COMSOL AbaqusFEniCS/ MOOSE ) and dielectric spectroscopy (BbDS/ TSDC ). Strong record of peer-reviewed publications in composite materials multiphysics modeling and/or data-driven approaches. Proficiency in programming and data analysis (Python MATLAB ). Experience in data-driven modeling (PINNs PyTorch NVIDIA Modulus) and cross-property correlation between electrical and mechanical degradation phenomena.Preferred QualificationsExperience with physics-informed machine learning neural operators and high-performance computing ( HPC ) environments. Hands-on experience with composite fabrication ( VARTM OOA compression molding) and microscopy techniques ( SEM AFM KPFM ). Familiarity with ASTM / ISO testing standards for composite characterization. Proposal writing experience with government or industry sponsors. Leadership or service roles in professional societies ( ASC SAMPE AIAA PHM ).Work ScheduleMonday-Friday; 8:00am-5:00pm Occasional travel to conferences collaborators and sponsor facilities. The position is Grant-funded and is expected to continue until 01/16/2032. Key Skills Laboratory Experience,Machine Learning,Python,AI,Bioinformatics,C/C++,R,Biochemistry,Research Experience,Natural Language Processing,Deep Learning,Molecular Biology Employment Type : Full-Time Experience: years Vacancy: 1