Data Scientist in astronomical imaging and machine learning

The Stochastic Information Processing Group (SIP) (http://sip.unige.ch) of Prof. Slava Voloshynovskiy and the Starbursts in the Universe group (https://www.unige.ch/sciences/astro/starbursts/en/) of Prof. Daniel Schaerer, University of Geneva, Switzerland, have an open data scientist in astronomical imaging and machine learning.

Profile:

• PhD degree in one of the following domains: astronomy, astrophysics, computer science, data science or mathematics
• Proven record of achievements and previous experience with machine learning and imaging, knowledge of signal/image processing and astro-imaging
• Strong programming skills in Python with experience in TensorFlow, PyTorch or Keras that will be verified
• Knowledge of HPC and large-scale data processing
• Strong verbal and written communication skills in English
• Strong analytical abilities and problem solving/troubleshooting skills

We offer:

• Great multidisciplinary team
• Collaboration with the leading groups in machine learning and astronomy in Switzerland
• Great learning opportunities
• Competitive salary including Standard Swiss Social Security, Accident Insurance and Pension contributions
• The position is initially for 2 years with an option of extension for 2 additional years

The successful applicant will be involved in a project on the development of machine learning algorithms and innovative data analysis tools in the scope of the Swiss participation in the Square Kilometer Array observatory (SKA, https://www.skatelescope.org). The project concerns the development of new machine learning methods for the SKA that include imaging algorithms and classification techniques. The data scientist will be also engaged in the development and testing of scalable high-performance code on the data from SKA precursors and providing the technical support of developed code to other groups within a Swiss collaboration.

The hiring groups at the University of Geneva have a broad network of national and international collaboration and scientific exchange, visits and training.

STARTING DATE
The envisioned starting date is February-March, 2022 but it can be negotiated.

APPLICATION PROCEDURE
Qualified candidates are encouraged to send their applications including a CV and publication list, description of research experience and interests, contact information of three references in a single pdf by email. Applications received by 15 December, 2021 will receive consideration.

Pre-selected candidates will be invited for a skype/zoom interview.

CONTACT
Please, send your applications to the emails below:
E-mails: [email protected] and [email protected]