Research Scientist - Bayesian Deep Learning for Active Learning
Business Unit: Corporate Technology
Requisition Number: 226772
Primary Location: United States-New Jersey-Princeton
Assignment Category: Full-time regular
Experience Level: Entry level
Education Required Level: Doctorate Degree
Travel Required: 10%
For nearly 170 years, pioneering technologies and the business models developed from them have been the foundation of Siemens‘ success. Our central research and development unit, Corporate Technology (CT) plays an important role in this. Together with our global network of experts, we are a strategic partner to Siemens’ operative units and provide important services along the entire value chain – from research and development to production and quality assurance, as well as optimized business processes. Our support provided to the businesses in their research and development activities is ideally balanced with our own future-oriented research.
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.
Be able to lead the research activities focused at applying combination of Deep Learning and machine learning techniques including supervised learning, unsupervised learning, generative models, reinforcement learning, probabilistic graphical models and knowledge representation pipelines to design, analysis and engineering workflows for real world problems (including applications on small data learning, active learning and online learning).
Research, design, and implement algorithms that can infer knowledge on highly heterogeneous, large-volume streams of data including image, natural language, and knowledge graphs.
Actively contribute towards research proposals and grant writing for the use of state of the art machine learning algorithms for engineering processes.
Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
Work with customers to understand algorithm requirements and deliver high-quality solutions.
PhD required in Machine Learning, Computer Science, Physics, Engineering or related discipline. PhD in machine learning (Bayesian inference, deep learning, reinforcement learning) is preferred.
5+ plus years of related experience.
Applied experience with any one or more of Natural Language Processing, Deep Learning, Bayesian inference, probabilistic modeling and reasoning tools is a must.
Skills in graphical databases, linked data framework development, optimization, probabilistic reasoning, uncertainty quantification and design analysis are a plus.
Contribution to research communities and/or efforts, including publishing papers at conferences such at NIPS, ICML, SIGRAPH, CVPR, ICCV, UAI, ACL, EMNLP, etc.
Outstanding written and verbal communication skills in English are required in combination with excellent analytical and interpersonal skills and can do attitude.
Successful candidate must be able to work with controlled technology in accordance with US Export Control Law. US Export Control laws and applicable regulations govern the distribution of strategically important technology, services and information to foreign nationals and foreign countries. Siemens may require candidates under consideration for employment opportunities to submit information regarding citizenship status to allow the organization to comply with specific US Export Control laws and regulations. Additional information on the US Export Control laws & regulations can be found on http://www.bis.doc.gov/index.php/policy-guidance/deemed-exports/deemed-exports-faqs?view=category&id=33#subcat34