PhD position in Machine Learning Applied to Large Area Perovskite Photovoltaic Modules
Tum
Munich, Germany
vor 6 Tg.

Upscaling of photovoltaic modules is an enormous challenge as many fabrication techniques used for small sizeprototype devices do not apply for large areas.

Moreover, one of the main foreseen application forperovskite PV-panels is to integrate them with current silicon technology in order to improve siliconlight absorption.

Thus, any fabrication protocol that wants to be integrated with silicon technologymust not interfere with the silicon components, a further constrain in the perovskite fabrication.

Thus the aim of the PhD project is to collaborate in strict contact with Prof. Di Carlo s (University of Rome) experimental group in order to transfer their experience into an automatized ML approach to identify the best way to produce large area perovskite PV-devices.

Artificial Intelligence and Machine Learning (ML) are becoming fundamental computational tools in

many different research areas, from more standard applications (image recognition, text translation)

to more exotic ones (medicine, drug discovery). Material science and device development is no

different. In the last few years a research field has been emerging aimed to apply ML methods for the

discovery and classification of novel materials for many different applications (photovoltaics,

electronics, thermoelectricity, energy storage, etc.).

In the present PhD project we aim to apply ML methods to help the synthesis and fabrication of

Photovoltaic modules using an innovative material : perovskite. The project is in collaboration with the

experimental group of Prof. Aldo Di Carlo at the University of Rome Tor Vergata . He has been working

since several years on perovskite photovoltaic technology. The activity of Prof. Di Carlo is about the

upscaling of these new devices from small prototype cells with an area of few square centimeters to

real PV-panels of larger dimension.

The upscaling involves an enormous challenge as many fabrication techniques used for small size

prototype devices do not apply for large areas. Moreover, one of the main foreseen application for

perovskite PV-panels is to integrate them with current silicon technology in order to improve silicon

light absorption. Thus, any fabrication protocol that wants to be integrated with silicon technology

must not interfere with the silicon components, a further constrain in the perovskite fabrication.

Thus the aim of the PhD project is to collaborate in strict contact with Prof. Di Carlo s group in order

to transfer their experience into an automatized ML approach to identify the best way to produce large

area perovskite PV-devices.

Hinweis zum Datenschutz :

Im Rahmen Ihrer Bewerbung um eine Stelle an der Technischen Universität München (TUM) übermitteln Sie personenbezogene Daten.

Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung.

Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise der TUM zur Kenntnis genommen haben.

Melde diesen Job
checkmark

Thank you for reporting this job!

Your feedback will help us improve the quality of our services.

Bewerben
E-Mail
Klicke auf "Fortfahren", um unseren Datenschutz-und Nutzungsbestimmungen zuzustimmen . Du kriegst außerdem die besten Jobs als E-Mail-Alert. Los geht's!
Fortfahren
Bewerbungsformular