Ofertas de trabajo

APPLICATION ID: ALL12 - APPLY NOW 

What we are looking for:

We seek to hire a highly motivated individual with training in either physics, chemistry, materials or computer science at Ph.D. level, preferably with experience in the use of ML/AI, and an interest in applying such techniques to problems in drug development and/or materials science. Familiarity with graph-based methods is desirable, but not essential. Candidates are encouraged to submit their own research programmes, describing their objectives, relevance, and how they would fit with research conducted at CIB and ICMM.

The context:

Over the last decade and a half graph-based AI methods have been developed in order to make it possible to use of Deep Learning approaches when working with objects that are intrinsically three-dimensional, hence not easily represented by regular data structures such as matrices. These developments have significantly extended the potential use of AI into the realms of drug design, chemistry and materials science, since molecules and atomic systems are very naturally described as graphs. With this project we aim to harness the power of graph-based AI methods to accelerate the process of obtaining new drugs and chemicals, and the design of novel materials with specific desirable properties.

The problem to address:

We have identified a particular topic of interest for the two groups involved in this proposal, namely that of Nuria Campillo (CIB Margarita Salas) and that of Eduardo Hernández (ICMM), which concerns the development of a graph-based model to develop AI-QSAR models for the prediction of biological properties such as toxicity (i.e. mutagenicity), ADME properties and biological activity. As an example, to begin would be the development of a graph-based model for the prediction of the outcome of the Ames mutagenicity test. This experimental test gauges the capability of a new compound or candidate drug to induce mutations in the recipient. We aim to develop a graph-based DL model to accurately predict the outcome of such a test on candidate new drugs. The availability of such a tool could significantly reduce costs and speed up the process of drug development in the pharmaceutical industry. This topic is well-defined and with clear objectives, and it is our feeling that it can be completed in a reasonably short time, thus opening the opportunity for the successful candidate to pursue new objectives in collaboration with the research staff at CIB and/or ICMM.
 

Objectives:

• To develop a graph-based AI model for predicting the outcome of the Ames test for given molecules (in collaboration with N. Campillo and E. Hernández).
• To explore potential uses of graph-based generative AI methods to automate the search for new materials with desirable properties.
• To actively engage in on-going research projects at CIB and ICMM in which ML/AI could be used.

Expected Outcomes:

The CIB and the ICMM are located in and around Madrid. Both centres provide vibrant research environments, offering the possibility to interact and collaborate with other researchers actively working in the general fields of medicinal chemistry and materials science. The successful candidate will be expected to identify potential use cases for AI methods/techniques in these fields and thus foster and expand the use of AI methods in these fields.

APPLICATION ID: ALL15  -- APPLY NOW

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 an international consortium involved in the development of an efficient scheme combining theoretical models together with fabrication and characterization tools to accurately explore Fe-based nanostructures in the search for the best permanent magnets. 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.
The associated partners: The proposal is part of a wider international research carried by a consortium composed by experimental and theoretical research teams and industrial partners. Our expertise covers different spectroscopies, (both simulation and experimental data acquisition), from those targeted to Fe systems (Mössbauer) to broader scoped ones as Raman or X-ray photoemission. 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 provide 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 magnetic nanostructures.
• To refine the interpretation of spectral signals based on the potential of ML algorithms to detect trends beyond human discrimination.

Expected Outcomes:

• To implement efficient ML algorithms in the exploration of 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.