Hardware-friendly Reservoir Computing for long-range propagation

16 Apr 2026
Job Information

Organisation/Company

Università di Pisa (UNIPI)

Department

Dipartimento di Informatica (Department of Computer Science)

Research Field

Engineering » Other

Researcher Profile

First Stage Researcher (R1)

Positions

PhD Positions

Application Deadline

31 May 2026 - 23:59 (Europe/Rome)

Country

Italy

Type of Contract

Other

Type of Contract Extra Information

Full time

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA

Marie Curie Grant Agreement Number

101226674

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Organization / Company: Università di Pisa (UNIPI)

Department: Dipartimento di Informatica (Department of Computer Science)

Research Field: Computer Science; Machine Learning; Engineering; Other

Researcher Profile: First Stage Researcher (R1)

Position Type: PhD Position

Application Deadline: 31 May 2026 – 23:59 (Europe/Rome)

Country: Italy

City: Pisa

Type of Contract: Fixed-term (36 months)

Job Status: Full-time

Offer Starting Date: 1 November 2026

EU Funding Programme: Horizon Europe – MSCA Doctoral Networks

MSCA Grant Agreement No.: 101226674

CUP: I53C25002280006

Related to Research Infrastructure: No

About the Position

The Department of Computer Science (Dipartimento di Informatica) at the University of Pisa is seeking a Doctoral Candidate (DC11) for a 3-year full-time research position within the EU-funded MINDnet Doctoral Network. The selected candidate will be enrolled as a supernumerary student in the PhD Programme in Artificial Intelligence at the University of Pisa (following a favorable decision by the Doctoral Board of the Doctoral Program in Artificial Intelligence), with a simultaneous research fellowship (Incarico di ricerca, Art. 22-ter, L. 240/2010), as part of the HORIZON -MSCA-2024-DN-01 Neuromorphic Computing and Signal Processing Training Network project (MINDnet).

 

About MINDnet

MINDnet (Neuromorphic computing and signal processing training network) is a Marie Skłodowska-Curie Actions (MSCA) Doctoral Network funded under Horizon Europe (Grant Agreement No. 101226674). The network trains 15 Doctoral Candidates across 10 partner organisations — 7 universities, 1 research centre, and 2 companies — in 8 European countries. All 15 PhD projects address neuromorphic computing and analog signal processing, targeting applications in communication, sensing, geolocation, space, and biomedical fields.

MINDnet pursues a holistic optimisation across three technological platforms — photonics, electronics, and biological neurons — from individual devices to system architecture, targeting energy-efficient, scalable neuromorphic AI.

DC11 – Research Project

Project title: Hardware-friendly Reservoir Computing for long-range propagation

Work Packages: WP2, WP4

Where to apply

E-mail

mindnetselection@di.unipi.it

Requirements

Research Field

Engineering » Other

Education Level

Master Degree or equivalent

Skills/Qualifications

Research Objectives:

Develop a neuromorphic-friendly, non-dissipative Reservoir Computing (RC) architecture Optimised for long-range information propagation, adaptable to both photonic and electronic hardware platforms.

Optimize the architecture for energy efficiency and real-time processing across various AI tasks, respecting hardware constraints.

Validate the system on AI benchmark tasks (text and image processing) and real-world geolocation applications, assessing performance and robustness.

 

Expected Results:

• A flexible RC architecture capable of long-range propagation, compatible with both photonic and electronic platforms.
• An energy-efficient system validated on Spinnaker2 and benchmarked on standard AI tasks and practical geolocation scenarios.

Secondment Plan

The DC will undertake three secondment periods at partner institutions (total: 9 months):

• DTU (Copenhagen, Denmark) — Supervisor: Prof. Fabio Da Ros — Months 12–14 (3 months) — Design and optimization of hardware-friendly RC architectures for photonic platforms.
• SpinnCloud Systems GmbH (Dresden, Germany) — Supervisor: Mahmoud Akl — Months 18–20 (3 months) — Electronic implementation on Spinnaker2; non-dissipative properties and suitability for AI tasks.
• Albora Technologies SL (Barcelona, Spain) — Supervisor: Dr. Pau Closas — Months 26–28 (3 months) — Testing and validation on real-world geolocation applications.

Travel and subsistence expenses for secondments will be reimbursed according to project procedures. All DCs receive the monthly mobility allowance regardless of their location during secondments.

Specific Requirements

Education Level

Master’s degree (laurea magistrale or equivalent, 120 ECTS) in Computer Science, Data Science, Physics, Mathematics, Computer Engineering, Electronics Engineering, Automation Engineering, Telecommunications Engineering, or a closely related discipline, obtained no more than six years before the application deadline. An equivalent degree obtained abroad, recognised as equivalent by the selection committee, is also accepted.

Required Skills and Qualifications

• Master’s degree as specified above
• Strong scientific/professional background suitable for research and innovation activities
• Excellent command of the English language (written and spoken)

MSCA Eligibility Requirements

Applicants must satisfy all of the following Marie Skłodowska-Curie eligibility criteria:

Doctoral Candidate status: the applicant must not have been awarded a doctoral degree by the date of recruitment.

Nationality: open to candidates of any nationality.

Mobility rule: the applicant must not have resided or carried out their main activity (work, studies, etc.) in Italy for more than 12 months in the 36 months immediately preceding the application deadline. Military service, short visits, and periods spent as a refugee under the Geneva Convention are excluded.

Non-EU citizens must hold or have applied for a valid residence permit covering the full duration of the contract at the time of signing.

To be eligible to participate in the selection process, applicants must be in full possession of their civil and political rights; furthermore, Italian citizens subject to mandatory military service must be in compliance with that obligation. Failure to meet these requirements will result in disqualification.

Preferred Qualifications

• Solid experience in Machine Learning, in particular modelling and simulation of recurrent neural networks or Reservoir Computing architectures.
• Knowledge of non-linear dynamical systems.
• Familiarity (even at a theoretical or modelling level) with neuromorphic hardware architectures or integrated photonics for signal processing.
• Proficiency in Python and/or C++, and familiarity with machine learning frameworks (PyTorch, TensorFlow, JAX, etc.).

 

Additional Information
Benefits

Compensation and Benefits

Gross salary: € 111.532,29 for the full 36-month duration of the contract.

In addition to the aforementioned amount, in accordance with the provisions of the MSCA Call, the holder of the position is entitled to: 

• Monthly mobility allowance: € 575,59;
• Monthly Family allowance (if applicable under Horizon Europe MSCA DN rules): € 401,29;
• Healthcare coverage for the full duration of the contract

 

Duration: 36 months (3 years), full-time.

Contract type: Research fellowship (Incarico di ricerca, Art. 22-ter, L. 240/2010), University of Pisa.

PhD enrolment: The selected candidate will be enrolled as a supernumerary doctoral student in the PhD Programme in Artificial Intelligence at the University of Pisa (https://phd-ai-society.di.unipi.it/ ), subject to approval by the Doctoral Board.

Selection Process

The selection follows the principles of the European Code of Conduct for the Recruitment of Researchers (open, transparent, merit-based, impartial, equitable, without conflict of interest). All candidates will be notified of the outcome.

 

Selection is merit-based and evaluated on:

• Review of submitted documentation (CV, motivation letter, transcripts, research proposal)
• Interview, where applicable

Selection process

Interested candidates are invited to send the following documents by email to:

mindnetselection@di.unipi.it  — Subject line: [MINDnet DC11] Application – [Applicant Name]

 

The application must be submitted no later than 31 May 2026 at 23:59 (Rome time).

 

Required documents (to be submitted as a single PDF or as separate attachments):

Motivation letter (cover letter), explaining your interest in DC11 and your relevant background.

Curriculum Vitae including personal details (name, date of birth, degree obtained, current position).

Academic transcripts and degree certificate(s) with official grade scale description (in English or with certified translation).

Brief research proposal (1–2 pages) outlining your approach to the DC11 research objectives.

List of publications and/or other scientific outputs (if any), with PDF copies of published articles where available.

 

Applications received after the deadline will not be considered. Candidates may be contacted for an interview as part of the selection process.

 

For further information about the position, please refer to the “CONTACT” section.

Additional comments

Further Information

Department website: https://di.unipi.it/

MSCA at UniPI: https://www.unipi.it/ricerca/sostegno-e-incentivi-di-ateneo/msca-seal-of-excellence-unipi/

Number of offers available: 1

Organization / Company: Università di Pisa – Dipartimento di Informatica

Country: Italy

City: Pisa

Postal Code: 56127

Street: Largo Bruno Pontecorvo, 3

Name: Prof. Claudio Gallicchio

Role: Associate Professor – Main Supervisor, DC11

Department: Dipartimento di Informatica, Università di Pisa

E-mail (for research/scientific purposes) : claudio.gallicchio@unipi.it

E-mail (for all other purposes related to the call): mindnetselection@di.unipi.it  

Website: https://di.unipi.it/

Work Location(s)

Number of offers available

1

Company/Institute

Università di Pisa (UNIPI)

Country

Italy

Geofield

Contact

Website

https://www.unipi.it/

STATUS: EXPIRED

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