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Mae'r hysbyseb swydd hon wedi dod i ben ac mae'r ceisiadau wedi cau.
Research Fellow - Electrochemical-Thermal-Mechanical Modelling of Battery Systems (110399-0325)
| Dyddiad hysbysebu: | 25 Mawrth 2025 |
|---|---|
| Cyflog: | £35,116 i £45,413 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 08 Ebrill 2025 |
| Lleoliad: | University of Warwick Campus, Coventry |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 2 ddiwrnod yr wythnos |
| Cwmni: | University of Warwick |
| Math o swydd: | Cytundeb |
| Cyfeirnod swydd: | (110399-0325) |
Crynodeb
We are undertaking research into how traditional Li-ion battery cell design can be optimised through the application of systems engineering, component modelling and data science.
Creating new models and predictive tools to improve battery performance, lifetime and safety.
We are therefore seeking a Research Fellow to join our team.
The RF will explore the boundaries of multi-physics and data-driven approaches. RF will be supporting the academic investigators to coordinate all aspects of the research, including experimental design, data capture, model creation and validation.
You will have unique access to the resources of our Energy Innovation Centre (EIC) and bespoke high-performance computing facilities.
The post is funded by BATTWin -- a European Council’s collaborative project with various partners in the EU -- as well as HVM Catapult in WMG.
The overall aim of the project is to investigate: (i) the underlying reasons why current Li-ion battery electrode performance in practice falls well short of theory due to the manufacturing process limitations, (ii) novel approaches to design cells, especially with specific safety criteria, (iii) how multi-scale models can be leveraged for the performance prediction in manufacturing and afterwards.
You will need a PhD in a relevant discipline, along with excellent computing and program skills in multiple languages and environments. You will also need a problem-solving ability to manage and analyse manufacturing datasets and the ability to publish high-quality academic outputs.
AI/ML and advanced data processing for tabular, time series and image data are required core skills, Battery and manufacturing knowledge are desired.
Creating new models and predictive tools to improve battery performance, lifetime and safety.
We are therefore seeking a Research Fellow to join our team.
The RF will explore the boundaries of multi-physics and data-driven approaches. RF will be supporting the academic investigators to coordinate all aspects of the research, including experimental design, data capture, model creation and validation.
You will have unique access to the resources of our Energy Innovation Centre (EIC) and bespoke high-performance computing facilities.
The post is funded by BATTWin -- a European Council’s collaborative project with various partners in the EU -- as well as HVM Catapult in WMG.
The overall aim of the project is to investigate: (i) the underlying reasons why current Li-ion battery electrode performance in practice falls well short of theory due to the manufacturing process limitations, (ii) novel approaches to design cells, especially with specific safety criteria, (iii) how multi-scale models can be leveraged for the performance prediction in manufacturing and afterwards.
You will need a PhD in a relevant discipline, along with excellent computing and program skills in multiple languages and environments. You will also need a problem-solving ability to manage and analyse manufacturing datasets and the ability to publish high-quality academic outputs.
AI/ML and advanced data processing for tabular, time series and image data are required core skills, Battery and manufacturing knowledge are desired.