Zur Startseite von DrArbeit.de,
der deutschlandweiten Stellenbörse für Diplomarbeiten und Doktorarbeiten


Lupe2

Archiv - Stellenangebot

 

Dies ist ein Angebot aus der Datenbank von DrArbeit.de

Um die Datenbank komfortabel nach weiteren Angeboten durchsuchen zu können, klicken Sie einfach oben oder hier.

 
Archiv-Übersicht     Angebot Nr. 14210

Angebotsdatum: 31. März 2021
Art der Stelle: Doktorarbeit / Diplomarbeit
Fachgebiet: Chemie > Angewandte Makromolekulare Chemie
Titel des Themas: PhD Position - Modelling of Dependence of the Ooperating Parameters on Degradation of SOC Stacks

Institut: Forschungszentrum Jülich GmbH
Adresse:
Frau Hartmann
Wilhelm-Johnen-Straße
52428 Jülich bei Köln
Tel.:    Fax.:
Bundesland: Nordrhein-Westfalen
Homepage: http://www.fz-juelich.de/SharedDocs/Stellenangebote/_common/dna/2021D-042-EN-IEK-14.html?nn=718260
E-Mail Kontakt: mail

Beschreibung: Conducting research for a changing society: This is what drives us at Forschungs­zentrum Jülich. As a member of the Helmholtz Association, we aim to tackle the grand societal challenges of our time and conduct research into the possibilities of a digitized society, a climate-friendly energy system, and a resource-efficient economy. Work together with around 6,400 employees in one of Europe’s biggest research centres and help us to shape change!

In order to limit global warming caused by greenhouse gas emissions, energy transition is a necessary and challenging task for our society. The transition to a sustainable energy system requires the deployment of new, efficient technologies for the conversion of greenhouse-gas-free secondary energy carriers produced from renewable energy sources. At the Institute of Energy and Climate Research – Electrochemical Process Engineering (IEK-14), we are at the forefront of these efforts. Solid oxide cell (SOC) technology will play an essential role in the energy transition, in view of its high efficiency and ability of energy conversion (from chemical to electrical, and vice versa). To achieve the high efficiency and long stability, understandings of the mechanisms relating to power losses in stacks and degradation phenomena are necessary for further material development and improvement in stack design.

We are offering a
PhD Position – Modelling of Dependence of the Operating Parameters on Degradation of SOC Stacks

Your Job:
Although various degradation mechanisms have been identified in the last decades, a reliable prediction of the lifetime or degradation of SOC is difficult due to complex dependence of the degradation on operating conditions, as well as lack of reproducible long-term testing results. Additional limits stem from the missing availability of complex diagnostic procedures in complete systems. In this work, the degradation behaviour and mechanisms of SOC stacks under different operating parameters (e.g. current density, voltage, fuel utilization, conversion rate, temperature, gas composition, impurities, etc.) should be studied systematically based on the previous testing and analysis results of Jülich’s F10-design stacks and newly obtained data as needed. State of the art machine learning techniques (implemented with Python) shall be evaluated and applied to elucidate degradation phenomena during operation with the ultimate goal of lifetime prediction and design of accelerated degradation tests. The feasibility of applying models for online testing in systems should be evaluated. The developed models will focus firstly on fuel cell mode and should be validated by designed stack testing under selected operating conditions.

Your tasks in detail:
- Primarily you will undertake the curation and conditioning of existing test data (incl. systematic analysis of the available testing results) and newly obtained data from specifically conducted tests to obtain training and test data (~20%)

Furthermore you will design specific stack tests for:
- Obtaining additional data to augment the existing test data for training and testing
- Validation of the developed models (this includes degradation analysis with the support of electrochemical impedance spectroscopy and post-mortem analysis [~30%])

Besides this you will develop models based on machine learning techniques (~50%) for
- Degradation analysis (e.g. cluster analysis and classification of events and conditions)
- Accelerated testing
- Lifetime prediction of SOFC stacks

Your Profile:
- Master’s degree in electrochemistry, chemical process engineering
- Theoretical knowledge and practical experience on machine learning techniques
- IT skills: Python and/or MATLAB required, experience with general ML frameworks (e.g. Keras + TensorFlow, PyTorch, scikit-learn) is preferred
General computer skills: MS Office, Windows, Linux, Origin (preferred)
- Experience on SOC and electrochemical techniques are preferred
- Flexible, self-motivated, goal-oriented and team-minded
- Fluent in English

Our Offer:
We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We offer ideal conditions for you to complete your doctoral degree:

- A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work
- Ideal conditions for balancing work and private life, as well as a family-friendly corporate policy
- A structured doctoral degree programme for you and your supervisors with a comprehensive training courses and individual opportunities for your personal and professional further development
- Special support for international employees, e.g. through our International Advisory Service
- Extensive company health management

The position is initially for a fixed term of 3 years. Pay is above average in line with 75% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60% of a monthly salary as special payment (“Christmas bonus”).

Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at www.fz-juelich.de/gp/Careers_Docs. Please also check our doctoral researchers’ platform JuDocs.

Forschungszentrum Jülich promotes equal opportunities and diversity in its employment relations.

We also welcome applications from disabled persons.

We look forward to receiving your application until 21.04.2021 via our Online Recruitment System:
https://www.fz-juelich.de/SharedDocs/Stellenangebote/_common/dna/2021D-042-EN-IEK-14.html?nn=718260

Questions about the vacancy?

Get in touch with us by using our contact form:
https://www.fz-juelich.de/portal/EN/Careers/JobKontakt/_node.html?cms_jobid=2021D-042

Please note that for technical reasons we cannot accept applications via email.

www.fz-juelich.de
Methoden:
Anfangsdatum: 31. März 2021
Geschätzte Dauer: 3 years
Bezahlung:
Papers:
Sonstiges:

Archiv-Übersicht