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# Job offer

- **Company:** Universitat de les Illes Balears
- **Location:** Palma, Spain
- **Salary:** €30,000.0
- **Contract:** Temporary contract
- **Skills:** Machine Learning, Gaussian
- **Published:** September 25, 2026
- **Apply:** https://ppi.uib.cat/inici/

## About the Role

Master Degree or equivalent, A Master's Degree in Physics or related disciplines.
Specific Requirements

Previous experience in quantum physics, photonics and quantum machine learning.

Language requirements:

English skills would be a plus.

Experience requirements:

Previous experience in photonic quantum machine learning is required.

Years of Research Experience
1 - 4, * Curricular merits in the field (max. 40 points).

 * Qualifications in the specialist area (max. 20 points), other (max. 5 points).

 * Video curriculum and, if applicable, personal interview (max. 20 points).

 * Experience in the field (max. 15 points). E.g.: > 3 years (10 points), < 3 years (5 points).

 * Other merits (max. +5 points). E.g.: recommendation letters.

Eligibility threshold: 60 points.

Candidates who hold the qualification corresponding to the required category in the job offer with a non-EU degree, if selected, must present a certificate of equivalence of their non-EU qualification to a European qualification, or proof of having applied for this equivalence, in order to proceed with their hiring.
Selection process

## Description

The core focus of the position will be exploring the implementation of quantum reservoir computing on photonic platforms.

Photonic systems offer a natural, scalable route to quantum reservoir computing. Within this framework, particular attention will be devoted to the role of non-Gaussianity as a computational resource: since classical simulation of Gaussian states and operations can be performed efficiently and they therefore offer no genuine advantage, non-Gaussian elements such as photon-added or photon-subtracted states, Kerr-type nonlinearities or non-Gaussian measurements will be analysed.

The successful candidate will investigate how different sources and degrees of non-Gaussianity affect learning performance, memory capacity and the robustness of photonic quantum reservoirs.