| Title | Education level | Speciality | Deadline | Duration | Unit |
|---|---|---|---|---|---|
| Feasibility of future QGP measurements at the LHC | Bac+3 | Physique corpusculaire des accélérateurs MC simulations |
30/06/2023 | 3 mois | DPhN/LQGP |
| Application of a new machine learning methods to 3D muon tracks reconstruction with deep neural network | Bac+4/5 | Instrumentation During this internship, the intern would need to: • Understand the concept of algorithm unrolling • Understand the current 3D reconstruction algorithm • Propose and train a proof of concept to unroll the 3D reconstruction • Perform a 3D reconstruction and evaluate the performance increase/decrease |
30/06/2023 | 3 mois | DEDIP/DEPHYS |
| Caractérisation de la chaîne de détection, projet G-LEAD | Bac+4/5 | Instrumentation |
11/08/2023 | 4 mois | DEDIP/LASYD |
| Development of an algorithm to find displaced vertices for the CLAS12 experiment | Bac+5 | Physique nucléaire |
05/06/2023 | 6 mois | DPhN/LSN |
| Estimation of carbon-14 excesses from galactic supernovae | Bac+5 | Astrophysique Numerical Analysis - Numerical simulation |
26/07/2023 | 3 mois | DAp/LEPCHE |
| Studying stellar surface rotation with (NASA) TESS mission data using wavelet filtering and deep learning techniques | Bac+5 | Astrophysique Stellar physics, Surface rotation, data analysis, wavelet filtering, deep learning |
09/05/2024 | 4 mois | DAp/LDE3 |
| Machine learning for pixel demultiplexing in a gaseous time projection chamber for muography applications | Bac+4/5 | Instrumentation During this internship, the intern would need to: • Understand how the TPC detector and its multiplexing works • Understand the current demultiplexing approach • Develop an implementation of a convolutional layer for hexagonal pixels • Develop and train a network to demultiplex the detector pixels |
30/06/2023 | 3 mois | DEDIP/DEPHYS |
