Course Coordinator: Delzanno Giorgio

Università degli Studi di Genova, Department of Informatics, Bioengineering, Robotics and Systems Engineering (Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi – DIBRIS) 

Comparative assessment procedure

QUALIFICATIONS/PUBLICATIONS AND INTERVIEW

Further information  
on how to present qualifications and publications

Curriculum vitae:  candidates must present a curriculum vitae with clear indications on the qualifications acquired with relative grades, research experience, publications, awards and work experience. 

Research project: candidates must submit a research project with clear indication of the following information: type of grant (University of Genoa/MUR, PNRR M. D. 118, PNRR M. D. 117, Place reserved for employees); research theme or macro-theme (see specific section of this notice); reference DIBRIS research group and company that cofinances the position.

Instructions and suggestions for the submission of the research proposal are available at the URL  https://csse.phd.unige.it/admission

Exam Syllabus

The comparative procedure consists of

- an assessment of the candidate qualifications

- an interview to verify background and knowledge of the candidate as well as motivations and goals of the research proposal submitted with the application.

Research Themes

The research project must contain a clear indication of the research topic, and, possibly, the DIBRIS research group of reference and/or company co-financing the position.

Research topic for n. 1 scholarship funded by University of Genoa/MUR:
 

• Research topics: The projects proposed by DIBRIS research groups for University of Genoa/MUR grants are described at the URL: https://csse.phd.unige.it/researchthemes. 
  Research projects which in any case fall within the research lines of the Computer Science curriculum (see https://csse.phd.unige.it/) will also be taken into consideration.

Research Topics for n. 4 scholarships funded by DIBRIS:

• 1 grant on “Optimisation-driven learning for image reconstruction in fluorescence microscopy” funded by the ERC-2023-STG project MALIN, supervisor Prof. Luca Calatroni.
• 1 grant on “Generative model-aware learning for image reconstruction in fluorescence microscopy” funded on by the ERC-2023-STG project MALIN, supervisor Prof. Luca Calatroni.
• 1 grant on "Statistics and Optimization Aspects of Machine Learning" funded by the FARE project “Reliable and efficient machine learning for inverse problems”, supervisor Prof. Lorenzo Rosasco.
• 1 grant on “Navigation using model-based and model-free reinforcement learning” funded by the ERC project “RIDING”, supervisor Prof. Agnese Seminara. 

Information on references

Candidates must choose no less than one and no more than three contact persons to support their candidacy.

These referents must be university professors or experts in the field.

It will be the referents' responsibility to send the reference letters, within the deadline of the call, to the attention of the Coordinator of the PhD course at the e-mail address: phd.compsci@dibris.unige.it. 

In the application for admission, candidates must indicate the name, qualification and place of employment of the contact persons chosen by them. The reference letter template is available at the URL: https://csse.phd.unige.it/admission

Language

English

Other Information