Research Associate

;, United Kingdom

Updated: 12 days ago
Location: Edinburgh, SCOTLAND
Job Type: FullTime
Deadline: 27 Apr 2025

Role: 

FTE and working pattern: 1 FTE, 35hrs per week, Monday – Friday

Contract: 12 months fixed-term

Holiday entitlement:33 days annual leave plus 9 buildings closed days (and Christmas Eve when it falls on a weekday)

Purpose of Role

The School of Energy, Geoscience, Infrastructure and Society at HWU is seeking an exceptional postdoctoral research associate to work on developing deep learning techniques for automatic seismic interpretation, fault detection, seismic image enhancement and alignment to other data sources (e.g., automated well-to-seismic tie). This project is industrially funded, and the successful applicant is expected to support two PhD students working on the same project. The project will build on recent methods developed for computer vision (image segmentation, image super-resolution, image generation and synthesis) and aims at developing fit-for-purpose deep learning techniques to ensure realism of the geological interpretations and preserve the inherent uncertainties in the seismic data. The applicant will undertake all the necessary research and development of algorithms using modern machine learning libraries (mainly pytorch) and over the course of the project duration (three years), the developed algorithms is expected to reach a medium to high TRL. 

The applicant will join a world leading research group at the Institute of GeoEnergy Engineering (IGE) at Heriot-Watt University (HWU) with opportunities for collaboration with researchers working on various machine learning and artificial intelligence applications. 

Key Duties & Responsibilities

The position requires collaboration within a multi-disciplinary research environment consisting of mathematicians, geophysicists, computational scientists, and engineers in support of the project.  Specific responsibilities include: 

  • Develop deep learning algorithms for seismic data processing, interpretation, and inversion 
  • Apply the developed algorithm to standard benchmark datasets as well as proprietary large-scale datasets provided by the industrial project funder 
  • Document and publish the research results in peer-reviewed journals 
  • Report and present findings at international conferences 

Please note that this job description is not exhaustive, and the role holder may be required to undertake other relevant duties commensurate with the grading of the post. Activities may be subject to amendment over time as the role develops and/or priorities and requirements evolve. A flexible working schedule may be required to meet all key duties and responsibilities. 

Essential & Desirable Criteria

Essential

  • The minimum required education is a Ph.D. in mathematics, geophysics, computational science and/or engineering with strong computational background.
  • Prior experience in developing novel deep learning algorithms 
  • Prior experience in seismic data processing and inversion 
  • High level competence in statistics and nonlinear optimization 
  • Strong track record of publications in high impact scientific journals
  • Demonstrated written and oral communication skills
  • Excellent programming skills (interpreted language python or julia and compiled languages C or Fortran) 
  • Good team player with excellent communication skills 
  • Good presentation skills and self-organised 

    • Desirable

  • Knowledge of modern software development techniques (version control, software testing and documentation) 
  • Prior experience on competitive machine learning tasks (e.g., Kaggle competitions) 

    • How to Apply

    Applications can be submitted up to midnight (UK time) on Sunday 27th April 2025.

    For any technical and informal queries, please contact Prof. Ahmed H. Elsheikh (a.elsheikh@hw.ac.uk ). Please reference the position title when corresponding about this position.

    Use our total rewards calculator: www.hw.ac.uk/about/work/total-rewards-calculator.htm  to see the value of benefits provided by Heriot-Watt University.


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