Job Vacancies

The research group for Methods of Battery Management at the Bavarian Centre for Battery Technology at the University of Bayreuth has an open fixed-term (full-time) position as

Research Associate (m/f/x) in the field of Multiscale Simulation of Electrochemical Flow Reactors with Machine Learning

to be filled as soon as possible. The application deadline is 31 December 2024.

Electrochemical flow reactors are a key enabling technology for the electrification of the chemical industry and the storage of renewable energy. Tailored design and operation require sophisticated models that consider processes across scales. Our goal is the development of advanced multiscale modeling techniques for the simulation of electrochemical flow reactors by expoiting novel machine learning techniques. The project is part of the priority program Machine Learning in Chemical Engineering (SPP 2331) funded by the German research association DFG.

Tasks

• Exploiting advanced machine learning methods for multiscale simulations
• Development of novel numerical methods
• Performing multiscale simulations of electrochemical flow reactors

Qualifications

• Completed university degree (Master's or Diploma) in Engineering, Applied Math, Physics, Scientific Computing or related fields
• Strong background in numerical simulations at continuum or atomistic scale
• Keen to use sophisticated machine learning methods
• Communication skills and a researcher mindset
  

Scientific Environment
As a PhD candidate or Post Doctoral researcher, you will have access to modern research facilities at the Bavarian Center of Battery Technology and a supportive research environment. You will also have the opportunity to collaborate with leading experts in the field and participate in international conferences and workshops.

Please find the complete vacancy notice and the link to the application portal here.

The Chair of Physical Chemistry V: Theory and Machine Learning located at the University of Bayreuth’s Bavarian Center for Battery Technology (BayBatt) is currently seeking to appoint a

Fixed-Term Research Associate (m/f/d) in the field of Autoregressive Machine Learning for Electrochemical Interfaces  

to commence as soon as possible.The application deadline is 31 December 2024.

Tasks:

• Develop novel autoregressive ML methods to describe the time-evolution of electrochemical interfaces
  
• Collaborate on integrating ML models into multiscale simulations of electrochemical flow reactors
• Develop novel methods for representation learning of chemical reaction networks
  

Qualifications:

• Completed university degree (Master's or Diploma) in Chemistry, Physics, Computer Science or related fields
  
• Strong background in numerical simulations at the atomistic scale and/or modern ML techniques
• Interest in pushing the boundaries of multiscale simulations
• Strong communication skills and a curious mind
  

Scientific Environment:
As a PhD candidate, you will have access to modern research facilities at the Bavarian Center of Battery Technology and a supportive research environment. You will also have the opportunity to collaborate with leading experts in the field and participate in international conferences and workshops.

Please find the complete vacancy notice and the link to the application portal here.

At the Chair of Electrical Energy Systems of the University of Bayreuth a full-time position as

Research Associate (m/f/x) in the field of battery technology

is to be filled as soon as possible at pay grade 13 TV-L. The position is provisionally limited to 3 years in accordance with the Wissenschaftszeitvertragsgesetz (German Academic Fixed-Term Contract Act) and is intended for the candidate's own academic qualification (doctorate). The application deadline is 12 January, 2025.

The Electrical Energy Systems (EES) research group and the Bavarian Centre for Battery Technology (BayBatt) are conducting research into the next generation of lithium-ion and post-lithium batteries. The engineering sciences are making a significant contribution to this through research into testing and characterisation methods, modelling, battery management and operating strategies.

Your area of responsibility
Responsibilities include active, independent research: developing models and methods, setting up, conducting and analysing experiments and publishing results. It is possible to complete a doctorate. On the other hand, participation in teaching (5 SWS) in the form of lectures and supervision of student work is a central task.

As part of research projects, electrical energy storage systems such as lithium-ion batteries are analysed and further developed in the research group. Your specific area of responsibility includes

• the modelling and parameterisation/training of lithium-ion and post-lithium-ion batteries through the use and further development of terminal voltage models (e.g. conductor path model, neural networks)
• the analysis and modelling of hysteresis behaviour in lithium-ion batteries with silicon-based anodes and in sodium-ion batteries
• the development of model-based methods of cell diagnosis and battery management, including condition estimation, prognosis and optimised operating strategies

Your profile
You have an above-average university degree in the field of energy technology, electrical engineering, automation technology or a comparable specialisation. You are interested in analytical and systems engineering issues relating to modern electrochemical energy technologies and have skills in programming and electronics, modelling and optimisation, measurement and control technology. Experience in the field of batteries or fuel cells would be desirable, but not essential. Your way of working is characterised by creativity and scientific thoroughness, resilience and perseverance, cooperation and communication skills.

What we offer

• Remuneration according to TV-L E 13, full-time position
• forward-looking and highly topical subject areas
• an innovative scientific activity in combination with application-orientated research and exchange with project partners
• a varied area of responsibility with a lot of creative freedom and personal responsibility
• Modern working conditions in a creative working environment
• Pleasant working atmosphere, team spirit
• Compatibility of family and career
• Flexible working hours and the option to work from home within the framework of the applicable regulations
• as well as other benefits of public service, such as JobRad, attractive additional pension scheme with the Federal and State Pension Institution, a wide range of health promotion programmes, e.g. health days, workshops, active break and the opportunity to participate in a very large number of different sports programmes as part of the general university sports programme
• off duty on 24.12. and 31.12. of a year in addition to the holiday leave

Please find the complete vacancy notice and the link to the application portal here.

The newly established research group „Information Systems Research, in particular on Connected Energy Storage“  at the Department of Law, Business, and Economics and the University of Bayreuth’s Bavarian Center for Battery Technology (BayBatt) is looking for a

Research Assistant (m/f/x)

to start as soon as possible.

Your responsibilities

• Support the preparation of data relevant for economic analysis, including from un-structured text data, web-scraped data, or geospatial data
• Conduct first statistical analyses
• Compile relevant economic policies and academic literature

Your profile

• Student of battery management, economics, business administration, information systems research (“Wirtschaftsinformatik”), or other programs with links to the research topic
• Interest in economic policy and green energy transition
• Basic knowledge of Python or R
• High level of motivation and self-organization

What we offer

• A young research group with opportunities to shape a newly establishing research environment
• Opportunity to learn methods of empirical research and data science while working hands-on on policy-relevant topics
• Flexible work hours, ideally 8 hours/week (depending on the availability of the candidate)
• Payment according to the official university tariff, depending on qualification

To apply, please send your documents to Prof. Dr. Marie-Louise Arlt, arlt@uni-bayreuth.de 

Application letter highlighting your motivation; one-page CV; grading transcript and certificates of potential completed studies. Applications will be reviewed on a rolling basis, until the position is filled.

If you have any further questions, please contact Prof. Dr. Marie-Louise Arlt, incoming assistant professor and head of the research group: arlt@uni-bayreuth.de . More information on her current research can be found at www.marielouisearlt.com or here.

Please find the full job vacancy here.