Introduction
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 previous cohorts:
- 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
Impact
“The award opened a wide world for me in computational social science for climate and sustainability. It led me to reach out to experts not in my domain, form a virtual consortium comprising reputable institutions like Caltech and Boston University, and facilitate interdepartmental collaboration within Cambridge. This consortium enabled me to produce this paper, published in Nature Portfolio Journal which attracted positive media attention during COP27". |
“I developed knowledge-exchange skills and improved communication of complex ethical concepts to people with diverse backgrounds including practitioners based in industry, and ethnographers researching industry contexts. I gained project management skills in the management of the grant and improved my research independence skills by developing innovative ideas and facilitating speaker talks across disciplines. This has set a foundation which I can continue to build on in my ongoing postdoctoral research journey”. |
“Through leading the Data Study Group (DSG) as part of award, I learned a great deal about leading a highly collaborative project. During the DSG week I also gained a crash course in leadership and directing research experience”. |
“Joining this scheme, I was a clinician with no experience of AI. Having participated, I feel I have gained knowledge in machine learning. This has enabled me to network and build relationships more efficiently”. |
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 |