Fellowship in Data Analyst and Machine Learning

CERN

Company Description

At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact and provides insights into the fundamental laws of nature. Find out more on http://home.cern.

Diversity has been an integral part of CERN's mission since its foundation and is an established value of the organisation. See how we’re improving this further here. The international and multi-disciplinary environment makes CERN a truly unique place to work and learn.

 

Job Description

We are collaborating with Hoffman La Roche in the framework of CERN OpenLab to explore possible synergies in data analysis and machine learning in High Energy Physics and Epidemiology. Within this framework we are studying possible application of Reinforcement Learning to the LHCb experiment and to Epidemiology.

Concerning the LHCb experiment we are studying full data interpretation. We are developing an agent whose task will be to assign particle identifications to secondary vertices, classifying probabilities of the various decays.

Regarding Epidemiology we will focus on multimorbidity patients. Guidelines for these patients are not clear and often contradictory, especially for oncology patients affected by serious chronic illnesses. 

The successful candidate will collaborate with researchers from the department of Physics and the Institute of Epidemiology and Biostatistics of the University of Zurich.

There are 2 projects available:

  1. For 12 months, implementing Gb Generative Models to be used in an existing simulation for both Public Health and High Energy Physics data. This requires good knowledge of Generative Models such as Generative Adversarial Networks and/or Variational Autoencoders.
  2. For 24 months, a project implementing various techniques to be used in an existing simulation for both Public Health and HEP data. We require a good knowledge of Reinforcement Learning.

 

Qualifications

Eligibility criteria

  • You are a national of a CERN Member or Associate Member State;
  • You have a Bachelor or Master’s degree in Computer Science, Engineering or other related field;
  • You have no more than 4 years’ experience after completing your highest diploma.

Essential skills and experience

  • Good knowledge of Machine Learning and Deep Learning;
  • Good Knowledge of Tensorflow and/or Pytorch;
  • Passionate about data analysis and enthusiasm to work in an interdisciplinary collaboration
  • Good knowledge of Generative Models OR Reinforcement learning

 

Additional Information

CERN would very much like to benefit from your expertise, commitment and passion. 

In return, CERN will provide you with:

  • An employment contract for between 6 months (minimum) up to 24 months, with a possible extension up to 36 months.
  • A stipend ranging from 5,321 to 6,606 Swiss Francs per month (net of tax).
  • Coverage by CERN’s comprehensive health scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
  • Depending on your individual circumstances: an installation grant, family, child and infant allowances as well as travel expenses to and from Geneva.
  • 2.5 days of paid leave per month.

Your Life @CERN

Get a glimpse of what it’s like to work at CERN: https://careers.cern/benefits and https://careers.cern/our-people

How to Apply:

You will need the following documents to complete your application:

  • A CV (Resume) 
  • Your most recent relevant qualification (Degree)

We recommend you add two recent letters of recommendation, giving an overview of your academic and/or professional achievements. You can upload these letters at the time of application if you have them to hand. You will also be provided with a link as soon as you have submitted your application to forward to your referees to upload their letters confidentially. Please note this must be done before the closing date.

All applications should reach us no later than 31 March 2022.

Apply