Scientific Computation and Machine Learning- Internship

Scientific Computation and Machine Learning Internship

Company Overview

Siemens Foundational Technology (FT) is the central R&D department of Siemens and thus has a key role in shaping the future of our Simulation and Digital Twin technology. FT acts as a strategic partner to support the executive units of Siemens. In consequence, the main research focus is on future technologies for industry, infrastructure, mobility, and healthcare. We are looking for an Intern who will work within our Design and Simulation of Systems research team in Princeton, NJ.

In this role you will work on-site in Princeton, NJ. The position is a full-time position for at least 3 months.

Role Summary

In this internship, we are looking for a motivated and talented student with experience in simulation, machine learning, and optimization methods. You will build engineering analysis and optimization workflows with the help of traditional numerical methods and machine learning methods. The internship provides a unique experience to contribute to innovative industrial applications while mentored by experienced research professionals in an international setting. The position is a full-time role for at least 3 months with option of a hybrid arrangement if appropriate.

Transform the Everyday!

Key Responsibilities

• Explore and build upon the latest ML-based protein/molecule modeling frameworks available,
• Integrate models into high-order workflows and pipelines, such as optimization and uncertainty quantification assessments.
• Test and benchmark the created models with third-party or publicly available data.
• Collaborate with domain and subject matter experts who seek to design and develop novel solutions for challenging real-world industrial problems.
• Document learnings and project progress and regularly present results to the research and consulting team. Publication of results is encouraged where possible.

Education and Experience

· Enrolled in a Computer Science, Electrical Engineering, Statistics, Applied Mathematics or a similar Engineering PhD program working on the use of AI approaches to Computational Biology.

Basic Qualifications

• Ability to quickly learn to use new technologies and frameworks.
• Experience with setting ML model training and testing/evaluating ML models.
• Graduate level courses in Statistics, Data Modeling and AI
• Basic understanding of the latest ML approaches and architectures.
• Hands-on experience with software development in Python.
• Flexibility and resourcefulness to work in a growing, dynamic, interdisciplinary team of specialists.
• Ability to work independently and manage time effectively.
• Proficiency in English (verbal & written).

Preferred Skills

• Hands-on experience with Scientific Machine Learning.
• Familiar with Linux systems and computational clusters (CPU/GPU).
• Basic experience in version control systems and agile development, among others.
• Real-world problem-solving and a hands-on, can-do mentality.
• Eager to present your proposals and results in front of a big audience.

Work setting

The position does require the person to be in the United States of America and hold a valid work permit for the US.

About Siemens:

We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resource-efficient factories, resilient supply chains, and smarter buildings and grids, to sustainable transportation as well as advanced healthcare, we create technology with purpose adding real value for customers.

Our Commitment to Diversity, Equity, and Inclusion:

We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society. Come bring your authentic self and create a better tomorrow with us. Learn more about our commitment to DEI here.

Protecting the environment, conserving our natural resources, fostering the health and performance of our people as well as safeguarding their working conditions are core to our social and business commitment at Siemens. They are an integral part of our Business Conduct Guidelines and our corporate strategy.

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