PhD Researcher: Physics‑Driven AI for Energy Storage Safety

Marie Skłodowska-Curie Doctoral Network Researcher in physics-based anomaly detection, diagnostics, and AI safety in energy storage systems. Within the framework of the project “Grid-scale Energy Storage: Imperatives for Accelerating the Green Transition (RESTORATIVE)”, funded by the European Union’s Horizon Europe’s Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 101227219, at Imperial College London we are seeking a candidate for a research assistant position.

The Researcher employed on this post will be expected to register as a PhD student and to submit their PhD thesis at the end of their research contract. The topic of the PhD thesis will be related and aligned with the research activities conducted in the project. Please note applicants must not already hold a PhD.

RESTORATIVE consists of 17 PhD students at 7 universities and 4 companies. The project has a total of 16 beneficiary and associated partners from 10 different European countries. All 17 PhD projects fall within the overall theme of grid-scale energy storage technologies, but investigate the topic from different perspectives, including technological development, reliability and security, integration and coupling, socioeconomic and environmental assessment, and policy and regulation.

The successful applicant will join the Resilient Information Systems Security group, led by Professor Emil Lupu. The activities within the project will benefit from synergies with other projects in the group as well as with other activities at the department. The main supervisor will be Professor Emil Lupu, and the co-supervisor will be Professor Chris Hankin.

Apart from the time at Imperial, two secondments are planned for the position, i.e. one academic secondment of approx. 6 months at Politecnico di Milano, Italy, and one for 3 months at an industry partner in Milan, Italy. In addition, there will also be project meetings and common gatherings and trainings with the other 16 PhD students in the doctoral network, including 3 summer/winter schools in Denmark and Italy and 2 online workshops.

What you would be doing:

• The position offers an exciting opportunity to conduct internationally leading and impactful research in anomaly detection, diagnostics, and AI safety and security in energy storage systems.
• The Research Assistant will be responsible for researching, implementing, and delivering novel machine learning models and anomaly detection systems for energy storage systems that are robust to adversarial attacks.
• We consider taking a “physics-informed” approach, improving explainability and aiming to impose an unacceptable trade-off to the adversary between being detected and reaching attack objectives.
• The postholder is expected to visit the network partners during secondments for a total duration of up to 9 months (6 months + 3 months in Italy).
• Additionally, the post holder is expected to participate in project meetings, outreach activities including, but not limited to, participation in public events and campaigns, as well as dissemination to popular press.

What we are looking for:

• To apply for this position, you must have a First Class or Distinction Masters level degree, or equivalent, in a relevant scientific or technical discipline, such as computer science or mathematics awarded by colleges recognized overseas universities/institutions.
• Knowledge and a keen interest in AI and machine learning are essential for this post.
• Please note applicants must not already hold a PhD.
• Overseas applicants for whom English is not the first language must provide evidence of a satisfactory command of English before commencing the programme.
• Due to the mobility rules of the Marie Skłodowska-Curie program, the applicant must not have resided or carried out their main activity (work, studies, etc.) in the UK for more than 12 months in the 36 months immediately before their recruitment date.
• As doctoral training networks aim at the recruitment and training of PhD students, the preferred candidates selected after the interviews will be required to apply and be accepted onto the PhD programme prior to receiving a contract of employment.

What we can offer you:

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive, and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information:

• Full-time, Fixed term contract to start 1st October 2026 until 30th September 2029.
• In addition to completing the online application, candidates should attach a full CV, a supporting statement outlining the motivations for applying for this project, and a PhD, relevant academic and professional experience to date, interests, future career aspirations, as well as the ability to gain from and contribute to the College community.
• A 3-5 page research statement outlining your views and knowledge in the general area of this post, research issues that you see would need addressing and your initial ideas on the research methodology (e.g. what sort of simulation or experimental techniques) would be suitable to address the research issues. The research statement should refer to existing scientific literature using bibliographic references and should be your own original work.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above. We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.

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