Unraveling Mössbauer spectra of Fe-based nanostructures
OPEN POSITION
APPLICATION ID: ALL15
What we are looking for:
We are seeking innovative research proposals focused on the development of ML algorithms to identify the detailed system features contributing to a specific spectroscopic signal. Our main interests are Mössbauer and Raman spectroscopies, where we can generate a hierarchical workflow of training databases combining from ab initio data to measurements.
The context:
There is an urgent need to replace rare-earth (RE) magnets by sustainable alternatives, Fe nanostructures emerging as versatile and abundant candidates. Our ability to improve their efficiency to the level of REs strongly relies on improving characterization techniques that enable fast but accurate exploration of the widest range of Fe-based systems.
The problem to address:
Our groups at ICV (Adrián Quesada) and ICMM (Silvia Gallego) lead international consortiums involved in the development of sustainable materials for different purposes. We are exploring novel systems based on innovative schemes that combine theoretical models together with fabrication and characterization tools. The proper interpretation of spectroscopic signatures is a bottleneck in this procedure. Our aim is to use machine learning (ML) algorithms to enable the identification of individual contributions to the spectroscopic signal, so as to provide an improved estimate of the material features giving rise to the spectrum.
On one hand, we are involved in finding alternatives to rare earth magnets, a crucial topic that involves geopolitical, economical and sustainability interests. Fe-based nanostructures emerge among the best alternatives. In this field Mössbauer spectroscopy is a unique targeted characterization technique, for which expertise is required for interpretation. Likewise, Raman spectroscopy offers great capability for advanced materials characterization. Both techniques would greatly benefit from machine-learning assisted analysis of the data to unequivocally assign phases to the spectra.
Stakeholders of our research include companies interested in the implementation of ML protocols in their magnet production chains (Encapsulae), and research partners with expertise on the development of spectroscopy techniques (IQF-CSIC, UCLouvain, Antioquia University
Objectives:
- To design a targeted ML method for a specific spectroscopy that can be trained on the basis of the appropriate database, enabling the efficient use of spectral signals to discriminate detailed features of materials when analyzing big datasets
- To refine the interpretation of spectral signals based on the potential of ML algorithms to detect trends beyond human discrimination.
Expected Outcomes:
- Efficient ML algorithms that explore large datasets for the spectral characterization of nanostructured magnetic materials, useful both for research purposes and innovative industrial processes.
- Ideally, the complete automation of the spectral analysis could enable the setup of an open access platform available to the research community, specialized in each type of spectroscopy.
Qualifications:
This position is particularly adequate for young postdoctoral researchers willing to lead their research project, preferably with knowledge on AI methods and Physics. The most valued aspects are:
- Strong background in Deep Learning methods.
- Programming skills in Python.
- Motivation to apply AI to solve problems in Physics.
What we offer:
The research will be carried out in leading research centres in the area of materials that are now implementing AI units. Our groups are internationally recognized in their fields and participate in a wide international network, including research and innovative industry partners
