CDD PhD student M/F – Palaiseau, Essonne

CDD PhD student M/F


Palaiseau, Essonne


PhD Position F/M Signal processing-based on the squared eigenfunctions of the Schrodinger operator. Le descriptif de l’offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

A propos du centre ou de la direction fonctionnelle
It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .
Biomedical signals are usually characterized by the presence of peaks that provide direct or indirect information on the physiological and metabolic state. Usually, these pulse-shaped signals require preprocessing such as denoising and artifact removals. Further analysis and processing might be required in some specific situations to allow for the extraction of pertinent information from these signals. In this context, a quantum-based signal processing method has been proposed in 1. This method called the semi‐classical signal analysis (SCSA) method decomposes the signal into a set of functions given by the squared eigenfunctions of the Schrödinger operator associated with its negative eigenvalues 1. Thus, and unlike traditional signal decomposition tools, the SCSA expresses the signal through a set of functions that are signal dependent, that is, these functions are not fixed and known in advance but are computed by solving the spectral problem of the Schrödinger operator whose potential is the signal to be analyzed. Accordingly, these eigenfunctions capture more details about the signal and its morphological variations 2. The objective of this research project is to study the mathematical properties of the SCSA and compare the approach qualitatively and quantitatively to state-of-the-art methods such as Wavelets and empirical mode decomposition methods. In particular, the student will use some quantum properties of the operator to propose new criteria for the selection of the semi-classical parameter a key design parameter of the method for both denoising and signal characterization. Applications to biomedical signals will be investigated with a particular focus on EEG signals for epileptic seizure detection and prediction and for monitoring the mental health of athletes.

We seek a student with strong background and expertise in signal processing and mathematics.
Knowledge in biomedical signals and learning algorithms is a plus.

Partial reimbursement of public transport costs
~Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
~Social, cultural and sports events and activities
~Access to vocational training
~Social security coverage


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