Postdoctoral Research Associate in Statistical Machine Learning for Computational Imaging

Purpose of Role

The School of Mathematical and Computer Sciences at Heriot-Watt University is recruiting a postdoctoral research associate in statistical machine learning for computational imaging. 

The position is open in the context of the 5-year research project “Learned Quantitative Stochastic Imaging”, led by Prof. Marcelo Pereyra with funding from a prestigious UKRI EPSRC Open Fellowship. This project aims to establish the mathematical and computational foundation for the next generation of breakthroughs in AI-based statistical imaging sciences. The goal is to improve dramatically the inferential power of future computational imaging systems, with a particular focus on advancing uncertainty quantification and model validation capabilities, which are essential for using restored image data as evidence for decision-making or as scientific evidence. This research will evolve and intimately combine modern computational imaging paradigms with ideas and techniques from statistical machine learning, especially around self-supervised learning and generative artificial intelligence. In addition to impactful research, the project features an ambitious stakeholder engagement program designed to inspire young people to pursue STEM careers, increase public understanding of uncertainty and risk, and engage the public with the ethical & sustainable AI agenda.

Appointment is for 4 months, with a start date in February 2026. We particularly welcome applicants from under-represented groups as well as applicants with caring responsibilities and others who often face nonvisible barriers. 

Please note that due to the short term nature of this position, the University will be unable to offer visa sponsorship in order to undertake this role.

Key Duties & Responsibilities

The postholder will conduct and disseminate original research aligned with the project, working closely with Prof. Marcelo Pereyra. They will benefit from a supportive and stimulating research group environment and culture, as well as from a comprehensive, individually tailored professional development plan. They will also have the opportunity to interact with leading scientists who collaborate on this project, including for example DR Andres Almansa (CRNS & Universite Paris Cite), Prof. Alain Durmus (Ecole Polytechnique), Dr. Abdul-Lateef Haji-Ali (Heriot-Watt University), Dr Tobias Liaudat (CEA Paris Saclay), Prof. Jason McEwen (University College London), Dr Julian Tachella (ENS Lyon), and Prof. Konstantinos Zygalakis (University of Edinburgh). 

Essential & Desirable Criteria

Essential

PhD in electronic engineering, machine learning, applied mathematical sciences, or a closely related field. Candidates must have submitted their doctoral thesis before starting in post.  A track-record of achieving significant research results in the field of computational imaging, computer vision, signal processing, or a related field, as demonstrated by primary contributions to publications in leading journals and conferences, proportionate to stage in career. Evidence of strong statistical machine learning skills, including robust mathematical and statistical foundations, and proficiency in Python programming and deep learning frameworks.  Experience in training deep learning models for image generation or image restoration.  Proven ability to write clean, efficient, and well-documented scientific computing code.  Evidence of creativity, self-motivation, and independent critical thinking.  Ability to work independently and collaboratively, as well as excellent time management skills including the ability to prioritise workload.  Excellent oral and written communication skills, with the ability to articulate complex research findings through technical reports and by oral presentation.  A strong commitment to adopting EDI principles in the workplace. 

Desirable

Experience working in a multi-cultural environment.  Experience developing software in a Linux environment.  Experience running numerical experiments on shared HPC facilities (e.g., GPU clusters). Experience in team collaboration in software development and awareness of tools and best practice for effective collaboration.  Experience supervising research (e.g., in the context of MSc dissertations).  A good understanding of the state of the art in artificial intelligence models for image generation, architectures and training paradigms.  Interest in research outreach.  Awareness of the Open Science approach.