ERC General Information 2021

ERC General Information

All information concerning the European Research Council (ERC) can be found on the ERC website https://erc.europa.eu/

Please see the following excellent instructional videos produced by the European Research Council which explain the general procedures behind the preparation of a proposal to the Starting Grant, Consolidator grant, or Advanced Grant calls.

Video 1: Step by Step Guide to the ERC Application Process

Step by Step to the ERC application process

This is a quick and simple video explaining which researchers are eligible to apply for European Research Council grants, at what points in their career, what type of grants we offer, how ERC grant proposals are evaluated - plus all the basics to help you decide when and how you could apply!

Video 2: How to get started with your ERC proposal

How to get started with your ERC proposal

In this video, we will walk you through 7 things to consider before applying for an ERC grant. You need to be strategic in your career, and writing an application takes a lot of time and effort. However, an ERC grant offers independence and recognition. You can research a topic of your own choice, with your own team. It will increase your visibility, help you access large facilities, buy the necessary equipment, attract the best team members and collaborators and bring additional funding.

Video 3: How to write Part 1 of your ERC proposal

How to write Part 1 of your ERC proposal

In this video, we will be talking about the more research-focused part of the ERC application. If you’re considering applying for an ERC grant, you may already know that an application is made up of three parts: part A, part B1 and part B2. Here, we will look into the 3 sections that make up the second part of your application, part B1, and give you some practical tips.

Video 4: How to write part B2 of your ERC proposal

How to write part B2 of your ERC proposal

In this video, we will talk about the scientific part of the proposal that is referred to as part B2. This is the part where the panel dig deep into your proposal. In this video, we will cover what to consider when writing part B2.

Video 5: How are ERC proposals evaluated?

In this video we will walk you through the ERC evaluation process. You might think that this is not very relevant, but it actually indirectly impacts on how you write your proposal. Imagine that you have sent your proposal. What happens next? What’s going on behind the scenes while you are waiting for a decision? Let’s go through the 7 phases of the ERC evaluation process.

Video 6: ERC Interview Process

ERC Interview Process

In this video, we will walk you through the ERC interview process, so that you can feel a little more prepared. At the end of the video, we will also give you 8 top tips for a good ERC interview. ERC interviews have quite a reputation and can be nerve-wracking. If you watch this video, you’ll have a much better appreciation of what you will be faced with.

ERC Projects

MLstrong - Solving the strong correlation problem in density functional theory via machine learned fully non-local functionals

Specific programme: ERC-2023-STG - HORIZON ERC Grant
UPV/EHU Partner Status: Beneficiary
UPV/EHU PI: Stefan Vuckovic

Project start: 01/10/2024
Project end: 30/09/2029

Brief description: Considering its unparalleled trade-off between accuracy and computational cost, density functional theory (DFT) is the most employed electronic structure method in fields that stretch from biochemistry to material science. A fundamental and long-standing hurdle precluding DFT from having high accuracy and predictive power accuracy across all chemistry is the inability of approximate DFT methods to describe important strong correlation electronic effects. The strong correlation problem limits the applicability of DFT for many technologically-relevant problems, as many critical chemical systems and processes are strongly correlated, such as catalysis involving transition metals with half-filled orbitals. In addition to transition metal catalysis, strong correlation is omnipresent in stretched chemical bonds, metal-organic frameworks, open-shell radicals, functional materials, etc.

In the current project, I aim to solve the strong correlation problem in DFT by combining new theory-derived features (ingredients) exhibiting full spatial non-locality for building DFT methods with machine learning (ML) to accelerate method development. These novel features, extracted from the exact treatment of strong correlations within DFT, radically differ from the ‘Jacob’s ladder features’ that are employed in nearly all state-of-the-art DFT methods. Their ‘fully non-local’ qualities expand the space in which improved DFT methods can be sought. I will use ML to unlock the potential of this space for solving the strong correlation problem by developing a hierarchy of methods, called MLstrong package, all targeted at the strong correlation problem. MLstrong will be efficiently implemented in an open-source electronic structure platform and tested against standard chemical datasets and against accurate data for strongly correlated problems; and finally applied to problems inaccessible to current DFT, such as reactions catalyzed by transition metal catalysts and metal-organic frameworks.