PhD Intern - Data Science & Machine Learning - Siemens
- Data Scientist
- Data Analytics
- Data Engineer
- Business Intelligence
This Intern in data science and machine learning will contribute to our research activities by applying modern data analytics and machine learning on variety of structured and unstructured data from wide area of different industries such as automation, energy, healthcare, building automation and mobility with the goal to improve business insights and help various business units to gain competitive advantage in their markets.
The Intern will be responsible for developing new algorithms, publications and code prototypes as proof of concept.
- Research, design, and implement algorithms that power knowledge inference and online recommendations, based on Deep Learning/machine learning to consume various types of data
- 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 design, engineering and analysis tasks and devise revolutionary approaches.
- Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
- Fast prototyping, feasibility studies, specification and implementation of data analysis product components.
Required Knowledge/Skills, Education, And Experience
- Candidate must have experiences in the field of Big Data Analytics, machine learning/deep learning.
- Currently pursuing a PhD in Computer Science, Information Technology or a closely related field preferred.
- Strong proficiency in data mining, machine learning, deep learning
- Strong proficiency in R/Python and good experience in Java
- Capability for quick prototyping
- Outstanding written and verbal communication skills in English are required.
- Excellent interpersonal skills and a can-do attitude
- Strong collaboration skills and ability to thrive in a fast-paced environment
- Flexibility and adaptability to work in a growing, dynamic team
- Ability to work with controlled technology in accordance with US export control law required. 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
Preferred Knowledge/Skills, Education, And Experience
- You can either have a proven track record of complex Big Data analytics 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, EMNLP, TACL, ICML or NIPS (publications at AAAI, UAI, , AISTATS, KDD, ICDM, SDM, SC, IPDPS will also be considered).
- In depth Knowledge in Software development in Java
- Strong proficiency in parallel computing & distributed algorithms (e.g. Map-Reduce, CUDA, GPU)
- Strong proficiency in Big Data tools and their configuration & setup.
- Proficiency in security and compliance analytics preferred.
- Previous experience or knowledge in the field of probabilistic reasoning, uncertainty quantification, dimensionality reduction, decision trees, and design analysis is preferred.
Siemens is a global technology powerhouse that has stood for engineering excellence, innovation, quality, reliability and internationally for more than 165 years. As a global technology company, Siemens is rigorously leveraging the advantages that this setup provides. To tap business opportunities in both new and established markets, the Company is organized in nine Divisions: Power and Gas, Wind Power and Renewables, Energy Management, Building Technologies, Mobility, Digital Factory, Process Industries and Drives, Healthineers and Financial Services. Our support functions are split into two organizations, Corporate Core and Corporate Services. These organizations provide essential services to better enable responsible and profitable growth.
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