Post-Doctoral Enrichment Awards (PDEA)


Inspired by the success of the Turing Enrichment Scheme for doctoral students, we established the Post-Doctoral Enrichment Awards (PDEA). This initiative offers small financial awards to facilitate postdoctoral activity nationally with the aim of enhancing the careers of a diverse group of data science and AI researchers and raising the quality and breadth of future research outputs. The Turing’s PDEA scheme is designed to provide opportunities to develop new skills, research independence and collaboration.

What the PDEA offers

The PDEA offers flexible funds to enable researchers to build on their current research, develop and grow activities related to the core aims of the scheme with support from the Turing community:

  • Active participation in the Turing’s online community for researchers in data science and AI.  
  • Access to training and research showcase events for the Turing community.  
  • Opportunity to apply for additional funding for community building activities and projects.  
  • Opportunity to contribute to the Turing’s growing activity in the national skills and research space.
  • £2,000 award for activity relating to the core aims of the scheme (see more below). 

Who can apply

We welcome applications from a broad range of academic disciplines and backgrounds, especially those whose research spans multiple disciplines and applications and aligns with the Institute’s research areas. These can include any aspects of AI and data science. To support continued diversity within data science and AI we encourage applicants from underrepresented backgrounds to apply.  Applicants must consider themselves as unestablished in their research career. For more senior postdocs this may also mean they are building their career in a new direction in response to data science/AI innovations.

Example activities from the cohort: 

  • Facilitating research collaborations for neuroimaging data in the context of Alzheimer's disease  
  • Running a Data Study Group on AI for democratic innovations  
  • Organising a multidisciplinary workshop that brings AI experts into dialogue with journalism practitioners 
  • Establishing an interest group on the topic of simulation-based/likelihood-free inference within the Turing.

Previous calls

The Alan Turing Institute Post-Doctoral Enrichment Awards 2022

The Alan Turing Institute Post-Doctoral Enrichment Awards 2021

Awarded Researchers

This is not an exhaustive list of all awardees, only those who opted-in to be visible on this page. Click on awardee names to connect with them further.

Awardee Name Organisation Research Interests
Claudio Coppolo Amazon Cognitive robotics; Activity recognition; Robot perception
Marko Tesic Birkbeck, University of London Cognitive science; Explainable AI; Causal inference
Alex Elliott Cranfield University Mechanical engineering; Structural dynamics; Model-order reduction
Namhoon Cho Cranfield University Control; Estimation; Optimisation
Tin-Yun Cheng Durham University Galaxy morphology; Intergalactic and circumgalactic medium; Machine learning
Calvin Tsay Imperial College London Computational optimisation; Machine learning; Process control
Mobarakol Islam Imperial College London Deep learning; Medical image analysis; Image-guided intervention
Ana Valdivia King's College London Artificial intelligence; Social justice; Migration
Klaas Wiersema Lancaster Astrophysics
Sian Brooke London School of Economics Computational social science; Data science; Gender
Debi Pattnaik Loughborough University Neuromorphic circuits; SNN
Gainbi Park Newcastle University Geographic data science; Geodemographics; Social vulnerability
Han Wu Newcastle University Federated learning; Differential privacy; Synthetic data
Tasos Spiliotopolous Newcastle University Computational social science; Human-computer interaction; Distributed ledger technologies
Maryam Abdollahyan Queen Mary University of London Bioinformatics; Health informatics
Hüseyin Küçükali Queen's University Belfast Public health; Epidemiology; Primary prevention
Chaoqun Zhuang The Alan Turing Institute Building energy efficiency; Digital twins; decarbonisation
Lorenzo Zanisi UK Atomic Energy Authority Bayesian optimisation; Active learning; Neural PDE solvers
Mohamed Ali al-Badri University College London Deep learning; Neural networks; Genomics
Namid Stillman University College London Cell biology; Simulation based inference; Generative models
Vasilis Stavrinides University College London Imaging; Bioinformatics; Advanced statistics 
Alessio Spurio Mancini University College London Cosmology; Machine learning; Bayesian Inference
Davide Piras University College London Astrophysics; Physics; Artificial intelligence
Kai Hou (Gordon) Yip University College London Exoplanets characterisation; Deep Learning; Data challenge
Manuel Lera Ramírez University College London Genetics; Genetic engineering; Web development
Tian Lan University College London GIS; Geographic data science; Spatial analytics
Cameron Shand University College London Unsupervised learning; Generative models; Neuroimaging
Niall Jeffrey University College London Artificial Intelligence; Data science for science, Generative models
Andrew Mitchell University College London Urban soundscape; Machine hearing; Smart cities
Qian Fu University of Birmingham Data integration; Railway; Travel behaviour
Yin Hoon Chew University of Birmingham Computational systems biology; Dynamical modelling; Hybrid modelling
Winnie Chua University of Birmingham Cardiovascular science; Cognitive function; Sensorimotor neuroscience
Karcius Day Rosario Assis University of Bristol 5G/6G networks; Optical networks; Smart city planning
Margreet Vogelzang University of Cambridge Experimental linguistics
Ramit Debnath University of Cambridge Climate action; Misinformation; NLP
Ed Harding University of Cambridge Neuroscience; Dementia; Transcriptomics
Bronwyn Jones University of Edinburgh Journalism; Communications; Artificial intelligence
Caitlin McDonald University of Edinburgh Climate risk models
Holly Warner University of Edinburgh Anthropology of technology; Digital ethnography; Technological futures
Scott Ogletree University of Edinburgh Health geography; Spatial data science; Quantitative methods
Holly Tibble University Edinburgh Prognostic modelling; Machine learning; Health record mining
John Stewart Fabila Carrasco University Edinburgh Spectral graph theory
Joao Gabriel Motta University of Exeter Artificial intelligence in geosciences; Unsupervised learning; Mineral exploration
Ziqi Li University of Glasgow Spatial analysis; Explainable AI; GIS
Koorosh Aslansefat University of Hull AI safety; AI explainability and Interpretability; AI trustworthiness
Matthias Wong University of Hull Digital humanities; GIS; Meaning-making
Yanis Boussad University of Leeds Computer science, Wireless networks and measurements; Human-centred data science
Yuanxuan Yang University of Leeds Mobility; Geodemographics; Geo-computation
Rafael Papallas University of Leeds Robotics; Robotic manipulation; Motion planning
Tamara Fletcher University of Leeds Palaeoclimatology; Palaeoecology; Novel-proxy development
Nicole Nisbett University of Leeds Sustainability; Society and climate change; Normative change
Noorhan Abbas University of Leeds Data mining; Text analytics; Data science.
Wisdom Agboh  University of Leeds Robot manipulation
Oliver Todd  University of Leeds Big data; Artificial intelligence; Cardiovascular ageing
Tatiana Alvares-Sanches University of Leicester Geospatial analysis; GIS; Machine learning
Chuan Fu Yap University of Manchester Bioinformatics; Health data science; Bayesian network
Mike Walmsley University of Manchester Astrophysics; Computer vision; Citizen science
Jack Muir University of Oxford Earth sciences; Geophysics; Scientific machine learning
Yang Hu University of Oxford Medical image processing; Computational pathology; Knowledge engineering
Raunak Bhattacharyya University of Oxford Human robot collaboration, Autonomous vehicles
Wenkai Xu University of Oxford Statistical methodology; Machine learning
Harish Tayyar Madabushi University of Sheffield Natural language processing; Computational linguistics; Machine learning
Elnaz Shafipour University of Southampton Artificial intelligence; Multi-agent systems
Anna-Stiina Wallinheimo University of Surrey Cognitive psychology; Human-computer interaction; Artificial intelligence
Thiago Sogo Bezerra University of Sussex Experimental particle physics; Neutrino detection
Kim Kirby University of the West of England Natural language processing; Machine learning; Emergency medical service 999 call triage
Aditi Shenvi University of Warwick Graphical models; Decision support; Analysis of missingness
Fabio Massimo Zennaro University of Warwick Machine learning; Causality
Gabriele Pergola University of Warwick Computer science; Natural language processing; Machine learning