1) Can I apply?
2) When is the call deadline?
3) What is the salary?
4) What is the purpose of the position? What can I expect to gain from it?
5) What will I be doing if I get selected?
1 - You can apply if you have completed a masters degree in a scientific discipline (physics, astronomy, mathematics, statistics, computer science) not earlier than one year ago. You are supposed to possess some programming skills, although your wish to learn is more important than your knowledge base.
2 - The deadline is October 16. The application process is simple, but you want to look into the electronic procedure early on to verify that you have the required documents.
3 - The salary is in line with the wage of Ph.D. students enrolled in the course in Padova. I do not know the net after taxation,but it is of the order of 1100 euros per month. This is not a lot of money, but it is enough to live by in Padova for a student. You won't get rich, but your focus should be to gain experience and titles for your future career!
4 - The purpose of the internship is to endow the recipient with skills in machine learning applied to fundamental physics research. Ideally, the recipient would be interested to apply for a Ph.D. at the University of Padova after finishing the internship, and the research work would be a very useful asset for his or her CV, along with the probable authorship of a publication in machine learning applications to particle physics; but the six months of work may be a good training also for graduate students who wish to move out of academia, to pursue a career in industry. The point is that what we will be working on together is a topic at the real bleeding edge of innovative applications of deep learning - something which will be invaluable in the future both in research and in industry. I will explain more what this is about below.
5 - If you get selected, you will join my research team, which is embedded in the MODE collaboration of which I am the leader. We want to use differentiable programming techniques (available through python libraries offered by packages such as Pytorch or TensorFlow) to create software that studies the end-to-end optimization of complex instruments used for particle physics research or for industrial applications such as muon tomography or proton therapy.
More in detail, we are currently tackling an "easy" application of deep-learning-powered end-to-end optimization which consists in finding the most advantageous layout of detection elements in a muography apparatus. Muon tomography consists in detecting the flow of cosmic-ray muons in and out of a unknown volume, of which we wish to determine the inner material distribution. This has applications to volcanology (where is the magma?), archaeology (study of hidden chambers in ancient buildings or pyramids), foundries (where is the melted material?), nuclear waste disposal (is there uranium in this box of scrap metal?), or detecting defects in pipelines or other industrial equipment.
To find the optimal layout we consider geometry, technology and cost of the detector as parameters, and we find the optimal solution by maximizing a utility function connected to how well the imaging is performed in a given time, and the cost of the apparatus and other constraints. So this is a relatively simple application of differentiable programming - you can pull it off if you model with continuous functions the various elements of the problem. If we manage to create a good software product we will share it freely, and then move on to some harder detector optimization problem (there is a long list of candidates). Actually, we are starting in parallel to study the optimization of calorimeters for particle detectors, which is a much, much more ambitious project which a fresh new Ph.D. student working with me, Federico Nardi, will investigate, again within the MODE collaboration.
So, if you are a bright master graduate and you want to deepen your skills in machine learning, please consider applying! If we select you, we will have loads of fun attacking these hard problems together!
If you need more information, please feel free to email me at dorigo (at) pd (dot) infn (dot) it. Thank you for your interest, and share this information with other potential applicants!
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