NEW (16 April 2024)

Application of AI in Empirical Analyses of Complex Atomic Spectra and Energy Level Structures

The spectra and energy levels of neutral and low ionisation stages of many-electron atoms (e.g. the iron-group and lanthanide elements) are of great interest in the spectroscopy of astronomical objects such as stars and kilonovae. For the meaningful interpretation of state-of-the-art high resolution astronomical spectra acquired with modern telescopes there is a requirement for highly accurate laboratory measured atomic level energies and spectral line wavelengths reference data. However, incompleteness in such atomic data still plagues astronomy.

An observed atomic spectral line gives us information on only the energy separation between two atomic energy levels and the likelihood of the transition. However, the level energies (relative to ground or ionisation) must be known in order to accurately determine the nature of the electron wavefunctions and produce meaningful spectral reference data. Empirically determining the exact level energies must be approached very carefully and theoretical calculations are used as guidance, because the complex spectra for a single ‘heavy’ element can contain up to tens of thousands of transitions.

This project will focus on exploring and developing novel machine learning methods to analyse high-resolution complex atomic emission spectra recorded in the laboratory. For example, investigating possible advantages of using neural networks over traditional methods in spectral line detection and fitting, or designing a reinforcement learning framework for determining level energies from matching simple sets of observed transition wavelengths and relative line intensities with corresponding theoretical predictions.

Further information: The candidate is expected to document the research and present significant or promising results to a wider audience. By the end of the project, the candidate should obtain a more holistic perception of scientific research, acquire background in atomic and experimental physics, and gain proficiencies in applying machine learning algorithms to complex real-world problems.

Preferred timing of the UROP: Summer vacation or starting during summer term if all degree programme commitments have been concluded.

Bursary: a bursary will be available to the student who obtains the UROP.

Contact details: Mr Milan Ding and Prof. Juliet Pickering, Physics Department, Space, Plasma & Climate community, Imperial College, South Kensington campus.

Please send your CV and academic transcript in an email cover letter to milan.ding15@imperial.ac.uk

Digital Electronics: The development of tools and techniques to help automate the design of digital circuits from high level specifications. The implementation of algorithms in reconfigurable hardware or combined hardware/software

Prof George Constantinides, Circuits and Systems Research Group, Dept of Electrical Engineering, Room 910, Electrical Engineering Building, South Kensington Campus. Tel: 020 7594 6299 Email: g.constantinides@imperial.ac.uk

Non-Destructive Testing: Components and structures in safety-critical applications must be tested before service and at intervals during their operating life to ensure that there are no defects such as cracks or delaminations present which could cause failure. The tests which are carried out must not damage the component and are, therefore, termed non-destructive. Many parameters which can give information about the integrity of components are measured but there is no universally applicable technique and several areas, such as adhesive joints, are not adequately covered by existing test methods.

Current research is investigating the potential of sonic vibration and ultrasonic measurements for the detection of defects. Opportunities are available in these areas.

Efficient and Secure Machine Learning: My research team focuses on the intersections between hardware, algorithms, and security in the Machine Learning world. I am interested in hosting students for a UROP research experience who would like to explore the efficiency and security aspects of ML systems.

A bursary will be available to the successful candidate.

A new generation of multi-physics models for material degradation and failure

There is an opportunity now to develop a new generation of simulation-based models that can predict material degradation. This is made possible by ever-increasing computer power and the development of new finite element algorithms that can enable simulating concurrent (coupled) physical processes such as mechanical deformation, chemical reactions, diffusion of species and material fracture; so-called multi-physics modelling. These enriched continuum computer models bring quantitative predictive capabilities by resolving the underlying physics while delivering predictions at a scale relevant to engineering practice.

There is an opening for one or more UROP positions to work for at least 10 weeks in developing and/or applying this new generation of multi-physics models to a variety of technologically-relevant problems: from predicting the lifetime of wind turbines to enabling a breakthrough in battery technology.

Skills and experience required: An interest in computer modelling (finite element analysis) and mechanics of materials. Previous experience with Abaqus or COMSOL is desirable.

The research crosses the boundaries of mechanical engineering, civil engineering and materials science, and the student is expected to have a strong interest in finite element analysis, materials engineering or fracture mechanics.

Contact: Dr Emilio Martínez-Pañeda, Dept of Civil and Environmental Engineering, 249, Skempton Building, South Kensington Campus. Email: e.martinez-paneda@imperial.ac.uk; T: +44 (0)20 7594 8188; W: https://www.imperial.ac.uk/mechanics-materials/

More details on the research can be found in the following YouTube video: https://youtu.be/TPBBN6QHlnw