Data Analytics Scientist – Siemens Healthcare syngo US Innovation group

[Yiqiang Zhan]

Position:
We have an opening for a research scientist position in Siemens Healthcare in Malvern, PA, in the areas of machine learning and data analytics for healthcare applications. We are hiring at a "Senior Scientist" level which is suitable for a PhD or Post-Doc graduate. For strong applicants with multiple years of working experience in the related fields, it is possible to move this position up to a "Staff Scientist" level.

You will join our innovation group in solving exciting and challenging research problems with real-world medical applications. Our group delivers medical image analysis software applications for medical data analysis and understanding, supporting clinicians and other medical professionals. We are well recognized for delivering cutting-edge intelligent solutions to Siemens 3D workstations and medical imaging scanners. Our group also has strong publication record in top tier journals and conferences.

Responsibilities:
• Contribute to research projects to develop medical data analytics solutions
• Advance the state-of-the-art technologies to solve large-scale and real-world data analytics problems
• Conduct fast prototyping, feasibility studies for exploratory clinical research
• Work with customers to understand clinical requirements and deliver proper solutions
• Support the productization of research prototypes

Education:
Ph.D. in Computer Science/Engineering, Statistics, Applied Mathematics, Electric Engineering, or related field.

Skills/Competency:
• Strong research capability in machine learning and data analytics, proved by publications in top tier journals/conferences.
• Research experience in mixed media processing is preferred.
• Experience in big data tools, e.g., Hadoop, is preferred.
• Experience in medical imaging is a plus but not required.
• Experience in NLP (natural language processing) and text analytics is a plus but not required.
• Strong programming ability is required.
• Ability to work independently and in a team environment.
• Excellent written and verbal communication skills.

To apply, please send your resume to yiqian...@siemens.com with the subject "Data analytics scientist".

[Alexandre d'Aspremont]

Position as data scientist
Chaire "Economie et gestion des nouvelles données"

● Location: within one of the lab of the chaire (Paris­-Dauphine, ENS Ulm, Ecole Polytechnique or ENSAE).
● Duration: 1 year renewable at least once.
● Salary: to be discussed depending on the applicant’s profile.
● Start: as early as July 2015.
● Application process: send a resume and a motivation letter to:

Alexandre d'Aspremont <aspr...@ens.fr>, Stephane Gaiffas <stephane...@cmap.polytechnique.fr>, Robin Ryder <ry...@ceremade.dauphine.fr>

Job description

The chaire "Economie et gestion des nouvelles données" is recruiting a talented young engineer specialized in large scale computing and data processing. The targeted applications include machine learning, imaging sciences and finance. This is a unique opportunity to join a newly created research group between the best Parisian labs in applied mathematics and computer science (Paris­ Dauphine, ENS Ulm, Ecole Polytechnique and ENSAE) working hand in hand with major industrial companies (AXA Global Direct, Havas, BNP Paribas, Warner Bros.). The proposed position consists in helping researchers of the group to develop and implement large­ scale data processing methods, and applying these methods on real­ life problems in collaboration with the industrial partners.
A non­ exhaustive list of methods that are currently investigated by researchers of the group, and that will play a key role in the computational framework developed by the recruited engineer, includes :
● Large scale non-­smooth optimization methods (proximal schemes, interior points, optimization on manifolds).
● Machine learning problems (kernelized methods, Lasso, collaborative filtering, deep learning, learning for graphs, learning for time­ dependent systems), with a particular focus on large­ scale problems and stochastic methods.
● Imaging problems (compressed sensing, super­resolution).
● Approximate Bayesian Computation (ABC) methods.
● Particle and Sequential Monte Carlo methods


Candidate profile

The candidate should have a good background in computer science with various programming environments (e.g. Python, Matlab, C++) and knowledge of high performance computing methods (e.g. parallelization, GPU, cloud computing). He/she should adhere to the open source philosophy and possibly be able to interact with the relevant communities (e.g. scikit-learn project). Typical curriculum includes engineering school or Master studies in computer science / applied maths / physics, and possibly a PhD (not required).


Working environment

The recruited engineer will work within one of the labs of the chaire. He will benefit from a very stimulating working environment and all required computing resources. He will work in close interaction with the 4 research labs of the chaire, and will also have regular meetings with the industrial partners.