Research Scientist - Optimization and Tool Automation
Business Unit: Corporate Technology
Requisition Number: 223871
Primary Location: United States-North Carolina-Charlotte
Assignment Category: Full-time regular
Experience Level: Mid level
Education Required Level: Master's Degree
Travel Required: 5%
We at Corporate Technology are more than employees: We are actively helping to make people’s lives a little better every day. Would you like to be a part of that? Then join us. We offer you a high level of practical relevance as well as an opportunity to individually contribute your knowledge and your visions around the world. Whether you’re helping to develop products for the operating units or working in interdisciplinary projects for the business areas: At Corporate Technology you’ll be working in the heart of Siemens’ technological research together with the best.
What are my responsibilities?
We are looking for an experienced professional in developing optimal automation tool chain for engineering & production multi-disciplinary optimization applications. You can either have a proven track record of multi-disciplinary optimization, design & engineering performance tool chain, digital manufacturing or optimization related work in industry, or research. For the former, please include your past projects. For the latter, please include your published papers, ideally at CVPR, ICCV, ACL, ICML, ASM, TMS, Applied Physics Journal, Acta Materialia or NIPS (publications at AAAI, UAI, AISTATS, KDD, ICDM, SDM, SC, IPDPS will also be considered).
This expert will be leading our research activities focused at extending computational techniques, methods and tools in domain of advanced manufacturing tool chain for design, engineering and production analysis for entire product lifecycle workflows.
Research, design, and implement algorithms that power knowledge inference and online recommendations, based on end-to-end simulation framework, multi-disciplinary performance analysis, novel high performance computing algorithms to consume design and engineering data in real-time.
Dive into huge, noisy, and complex real-world behavioral data to produce innovative analysis and new types of predictive models of engineering behaviors and manufacturing processes performance.
Explore the untapped potential of big data for engineering, manufacturing and service analysis tasks and devise revolutionary approaches (should this be all 1 bullet?)
Development of multi-disciplinary tool chain for engineering and performance optimization, geometry parametrization.
Combined use of classical and first-principles based co-simulations methodologies to study and develop prediction tools to establish the cause of the drift of the performance in field or test and/or production variations
Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
Apply deep learning techniques to large-scale, real-world problems with proven collaboration experience with university and industry.
Fast prototyping, feasibility studies, specification and implementation of data analysis product components
Working with customers to understand algorithm requirements and deliver high-quality solutions in timely manner. Project planning, cost approval and proposing innovative problem solving solutions to business unit technical management
Required Education, experience and skills: Preferred Experience and Skills #LI-MD1
Required Education, experience and skills:
Preferred Experience and Skills