New UROP opportunities will be listed here for one month, and thereafter will appear on the relevant faculty page (UROP website) until notified otherwise by the relevant member of academic staff.

Please note: The vast majority of advertised UROPs are advertised within academic departments for an internal audience (Imperial undergraduates) OR (and this is the route taken for most UROPs) are sourced by an individual student (whether an Imperial undergraduate OR an eligible undergraduate at another university) through direct contact with academic/research staff. Therefore this NEW OPPORTUNITIES page is normally used only by research groups who are looking to widen the audience for any UROP they are planning.

 

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.

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.

Skills and experience required: Programming skills in python (for PyTorch or TensorFlow) and knowledge/experience in machine learning algorithms are required. Knowledge/interests in atomic physics, high-resolution spectroscopy, uncertainties, and plasma physics would be bonuses.

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