Job offer PhD position: Synthesis of new active materials for flow batteries

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. Project objectives:

• 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 Sustainable Reaction Engineering group, headed by Prof. Alexei Lapkin (lapkingroup.com), is looking for a research assistant, to be enrolled to study for a PhD degree at the University of Cambridge, in high throughput synthesis of active organic molecules for flow redox batteries. The role holder will be responsible for developing the synthetic routes, setting up experiments for high-throughput synthesis in droplets (in flow), delivery of 3-5 target novel molecules and evaluating the environmental metrics of their synthesis.
  
  Master Degree or equivalent, Having a master degree or equivalent diploma, and not having a doctoral degree.
• First degree in chemistry or chemical engineering.
• Interest in novel synthesis technologies, such as automated synthesis, flow chemistry and high-throughput synthesis.
• Experience with/(interest in) computation, coding (Python) and machine learning.
  
  The advertized subproject is fully funded by the Marie Skłodowska-Curie European Training Network "PREDICTOR". It will be carried out by one doctoral candidate at the University of Cambridge, UK over a period of 36 months., The University of Cambridge is one of the world's oldest and most successful universities. We are a renowned centre for research, education, and scholarship that makes a significant contribution to society. The University is consistently ranked amongst the top universities in the world, and our affiliates have won more Nobel Prizes than any other University. Working at Cambridge you will join a diverse, talented and innovative community, with more than 18,000 students and over 16,000 staff from all walks of life and corners of the world.
More details are provided at: https://www.jobs.cam.ac.uk/job/48670/file/NQ43593+-+Further+Particulars.pdf Within the PREDICTOR project, 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 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. Eligibility criteria Mobility: The applicant must not have resided or carried out her/ his main activity (work, studies etc.) in the UK for more than 12 months in the past 3 years. Selection process