The OPVStability Marie Skłodowska-Curie Actions Doctoral Network (MSCA-DN) is an interdisciplinary research training network of 10 beneficiary universities from 7 countries. It is funded under the “Excellent Science” cluster of the Horizon Europe program. OPVStability aims to develop the Doctoral Candidate’s scientific expertise to produce excellent research, and interdisciplinary collaboration with scientists from different fields ranging from Physics, Chemistry, Materials Science and Data Science.

Roles and Responsibilities

The main part of your work will be carried out at the University of Bayreuth (Bayreuth, Germany, in the group of Harald Oberhofer), and secondment placements will take place at Friedrich Alexander University Nürnberg Erlangen (Germany, 2x one months) and Consejo Superior deInvestigaciones Científicas (Spain, one month).

The PhD project is funded for three years and would start by 1^st November or later.

Your PhD degree will be awarded on the basis of successful completion of the research work. You will also be required to participate in the training activities and workshops organized by the OPVStability MSCA-DN. As a Marie Skłodowska-Curie Actions fellow, you are also expected to contribute to the dissemination of your project’s result through public engagement and other scientific platforms.

Topic: Data-Driven OSC designed aimed at device stability

This PhD research will focus on:

i) Development of computationally accessible descriptors for the stability of OPV

ii) Training of surrogate models for OPV stability based on theoretical and experimental data

iii) Use of surrogate models for the discovery of stable OPV materials

The ideal candidate

• The applicant should have completed at least four- or five-years education with a Master’s degree or Diploma from a recognized university in Physics, Chemistry, Materials Science or in a related field and should be eligible as graduate student at the University of Bayreuth.

• The applicant should show interest in conducting a research project independently and should enjoy the work in an international, interdisciplinary environment.

• At the time of recruitment, the applicant must not have resided (or carried out his/her main activity e.g. work, studies, etc.) in Germany, for more than 12 months in the last 3 years immediately prior to the reference recruitment date.

• The applicant should have a background in density functional theory and charge transport theory and at least basic programming skills (preferably Python). First acquaintance with machine learning techniques and the Linux OS are welcome.

• The applicant should have an excellent command of the English language at a proficient level (spoken and written).

We offer a gross salary of at least € 40,000 for three years. The University of Bayreuth values the diversity of its employees and is explicitly committed to the goal of gender equality. Women are strongly encouraged to apply. Applicants with children are very welcome. The University of Bayreuth is a member of the Best Practice Network "Familie in der Hochschule e.V.", and has successfully participated in the HRK audit "Internationalization of the University". Persons with severe disabilities will be given preferential consideration if equally qualified.

Application Procedure

To apply for the position, kindly provide: (i) a letter of motivation including a 1-page statement of your research interests, relevant skills and experience; (ii) a CV including publication list, and copies of relevant certificates and (iii) names and contact details of three referees willing to write confidential letters of recommendation. All materials should be attached as a single PDF file (max. size 5MB).

For interested applicants, please submit your application to our job portal using this link: https://opvstability.breezy.hr/

Deadline:

The closing date for all applications is 15.10.2023.

Enquiries:

For further information about OPVStability and the Doctoral Candidate positions, you may contact our Project Manager, Dr. Julirose Kourist at [email protected]