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Our current openings
The PhD Fellow position is a unique position that pairs AM’s innovative approach to self-driving laboratory creation. with world class academic laboratories across Europe. As a PhD Fellow, the candidate will work as an engineer at Accelerated Materials while also obtaining a PhD at the University of Cambridge, over a period of 36 months.
The position will be fully funded by the Marie Skłodowska-Curie European Training Network “PREDICTOR“.
This PhD position offers a unique opportunity to contribute to cutting-edge research in the field of energy storage. The successful candidate will play a pivotal role in developing novel materials and processes for Vanadium flow batteries, a promising technology for large-scale energy storage applications. Specifically, the candidate will develop a unified framework for the robotic optimization of flow batteries, working across multiple groups and institutes, to create a coherent and easily transferrable approach – from idea to industrialization.
PhD Objectives:
Planned Secondments: The Candidate will take part in secondments at partner institutions in the grant-funded project, which may require travel.
Qualifications:
-A Master’s or Bachelor’s degree or equivalent in Chemical Engineering, Mechanical Engineering or a related field.
-Proficiency in data analysis and programming in Python.
-Strong background in laboratory-based chemical research.
-Excellent communication and teamwork skills.
-A passion for research and a desire to contribute to sustainable energy solutions.
Additional Skills (Desired):
-Experience with flow battery technology.
-Knowledge of laboratory and industrial automation techniques
-Familiarity with electrochemical analytical techniques such as cyclic voltammetry.
-Strong team-working ability, with knowledge of Agile project manaegment methodologies
Responsibilities:
-Develop software protocols for running automated tasks, manipulating experimental variables, transferring data, running models and user interfaces.
-Test software in the laboratory and in collaboration with project partners.
-Implement protocols to run holistic optimization studies across the development cycle of flow batteries.
-Analyse and interpret optimization results using multi-scale modelling techniques.
-Collaborate with project partners to ensure effective data and code sharing.
-Prepare scientific reports, publications, and presentations.
-Contribute to the development of intellectual property related to the project.
-Attend conferences and workshops to disseminate research findings.
-Maintain a high level of academic integrity and adhere to research ethics guidelines.
Background information
Marie Skłodowska-Curie Doctoral Networks are joint research and training projects funded by the European Union. Funding is provided for doctoral candidates from both inside and outside Europe to carry out individual project work in a European country other than their own. The training network “PREDICTOR” is made up of 22 partners, coordinated by Fraunhofer ICT in Germany. The network will recruit a total of 17 doctoral candidates for project work lasting for 36 months.
PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. It will enable the rapid identification, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments.
To validate the PREDICTOR system, the case study will be active materials and electrolytes for redox-flow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.
– A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems.
-Automated chemical synthesis, electrolyte production and characterization methods, so that the chemicals identified in the screening step can be rapidly produced and tested for their suitability in energy storage applications.
-Artificial- intelligence- based self-optimization methods that allow experimental data from material characterization to be fed back into automated experimental methods to enable self-driving laboratory platforms and for modelling and simulation tools, improving their accuracy.
–Data management systems to standardize and store the data generated for further use in model validation and self-optimization processes.
The recruited researcher will have the opportunity to work as part of an international, interdisciplinary team of 17 doctoral candidates, based at universities and industrial firms throughout Europe. She/he will be supported by two mentors within the PREDICTOR project, and will have multiple opportunities to participate in professional and personal development training. Through her/his work she/he will gain a unique skill-set at the interface between modelling and simulation, high-throughput experimentation / characterization and self-optimization and data management over different length scales from nano to the macroscopic level.
She/he is expected to finish the project with a PhD thesis and to disseminate the results through patents (if applicable), publications in peer-reviewed journals and presentations at international conferences.
By combining cutting edge research and supervision at the University of Cambridge in the Department of Chemical Engineering, with industrial experience, this position gives candidates a wholly unique experience to develop a comprehensive and rewarding skill set.
Accelerated Materials works to provide an inclusive environment to maximize employee wellness, with initiatives such as remote work programs, flexible hours, a generous leave allocation, and flexible arrangements for families. We also provide expert mentorship in entrepreneurship for candidates interested in commercialization of their inventions.
– In accordance with the European Union’s funding rules for doctoral networks, applicants must NOT yet have a PhD
-The applicant must not have resided or carried out her/ his main activity (work, studies etc.) in the United Kingdom for more than 12 months in the past 3 years.
Please send your CV by e-mail (preferred) or by post, quoting the reference PREDICTOR_PHD
careers@acceleratedmaterials.co.uk
Application deadline: 10th December 2024
It’s a chance to make a meaningful impact on industries ranging from healthcare to renewable energy and beyond. Your ideas and contributions will shape the future.
We’re at the forefront of innovation, constantly pushing the envelope of what’s possible. You’ll have access to state-of-the-art facilities and technologies, and a supportive team that shares your passion for innovation.
We believe that the best solutions come from diverse perspectives. Here, you’ll collaborate with experts from various fields, fostering creativity and innovation.
As a startup, we’re always evolving. Your career can evolve with us. We offer continuous learning and development opportunities and a clear path for advancement.
We’re committed to creating a workplace where every voice is heard, and every idea is valued. You’ll be part of a team that champions diversity, equity, and inclusion.
If you’re excited by the prospect of joining a dynamic, inclusive, and innovative team, we encourage you to explore our current job openings or join our talent pool. Your journey with us starts here, but the opportunities for growth and discovery are limitless.
