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Research Scientist - Causal Inference for AI at Siemens AG (Princeton, NJ)

Do you dream about better ways to understand what causes observed phenomena? Is catching incorrect conclusions derived out of correlated data exciting to you? Are forward casual inference and reverse causal questions motivating your research work?

SIEMENS Corporate Technology is looking for a talented Software Engineer with deep know-how and interest in Numerics, who would like to join our Product Design, Modeling and Simulation (PSM-US) Research Group of the “Simulation and Digital Twin” Technology Field, in the Tri-state area.

We need your help to build the world's most advancedmodeling, simulation and optimization technology stack for large scale AI-driven engineering and manufacturing toolchains , in collaboration with our partner startups, leading academic institutions and government agencies.We're currently bringing onboard talented scientist like you to research and build the algorithms powering our AI engine to support engineering workflows with common-sense and causal inference!

The team

Our team is composed of makers, innovators, engineers and scientists with deep technical expertise, who are passionate about disruptive technologies in the areas of Modeling, Simulation, Engineering, Control, Machine Learning and Optimization. Our deliverables enable the successful transformation of the technology trends in Digital Twininto the business of the future for a multitude of customer products and services.

Experienced candidates in these areas as well as those with advanced degrees who have research background in these topics with the skills and interest in applying their research and introducing innovative technologies into Siemens products are encouraged to apply!

PSM-US Mission

We want to teach machines to model, simulate and optimize parts, products and systems better than ever before and become perfect companions for engineers in need to accelerate by 1000x their workflows. Our mission is to free engineers from the burden of repetitive and non-creative tasks and allow them to truly explore the immensity of their design spaces, efficiently and with confidence in the results.

The Location

Our team is located in the wonderful Princeton NJ, a university town packed with outstanding international talent that provide a unique feel to this true cultural gem in the state. The town has plenty of activities to offer, but for those looking for more, at just about 1-h drive wehave NYC or Philly! We have the best public schools in the country and all of the above glued together by a very active and welcoming community!

Responsibilities

The candidates will be responsible for:

  • Lead the research activities focused on topics that are at the intersection of machine learning and causal reasoning, with technology startups, leading academic institutions and government agencies,
  • Leveragecausal insights to enhance the generalizability, fairness, performance or other properties of machine learning methods.
  • design, prototype, implement, evaluate and improve the state of the art causal inference, counterfactual prediction, and causal discovery algorithms using machine learning methods.
  • develop algorithms for applications of causal reasoning in engineering, physics, internet and other domains.
  • Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
  • Communicate complex ideas and testing results effectively, both orally and in writing, and provide recommendations and support to internal engineering teams through accurate and effective written documentation
  • Seeking advocacy from Siemens business units on potential use cases, while educating and transferring technologies back to Siemens businesses for product implementations.
  • Collaborating with others, both within and outside Siemens, to develop successful research proposals for external funding that align with Siemens strategic direction 
  • Required qualifications

  • PhD in Statistics , Mathematics, Computer Science, Physics, or equivalent, with 2+ years of related experience, with a well-established research track record as demonstrated by publications and open source software. Strong background in Machine Learning.
  • Min of 3 years of Graduate research and internship experience in Causal Reasoning and Machine Learning.
  • Proven ability to develop new research ideas as demonstrated by a strong publication record and early developments to the level of a working system prototype.
  • Strong theoretical and practical background in statistical modeling, statistical pattern recognition, machine learning, sparse methods, applied mathematics, optimization.
  • Outstanding coding skills and ability to write high quality code in Python, R and C++, “code it right the first time”
  • Good communication and organization skills, with a logical approach to problem solving, good time management and task prioritization skills, with motivation to learn and use new technologies, work under uncertainty at fast pace, and ability to multitask and make key contributions to several projects.