| J.-H. Nowé | Countering illegal timbering by using multimodal machine learning | 2022 - 2023 |
| N. Tourne | Two-branch neural networks for predicting protein-DNA interaction | 2022 - 2023 |
| Y. Van Laere | Detection of 5mC modification in Nanopore sequencing data using deep learning | 2021 - 2022 |
| E. Lorrez | Quantifying the environmental controls on African tropical forest dynamics through using a Granger causality framework | 2020 - 2021 |
| L. Theunissen | A comparison of flat and hierarchical classification for automatic annotation of single-cell transcriptomics data | 2020 - 2021 |
| T. Willaert | Improving classification by rejection of high epistemic uncertainty points | 2020 - 2021 |
| K. Zhang | Drug-target interaction prediction using multi-target prediction methods | 2020 - 2021 |
| L. Davey | Using artificial neural networks to uncover features in promoter sequences responsible for nonorthogonality in E. Coli | 2019 - 2020 |
| B. De Saedeleer | Combatting illegal timber trade using chemical fingerprints: the power of mathematics and mass spectrometry | 2019 - 2020 |
| L. Pollaris | Protein secondary structure prediction using transformer networks | 2019 - 2020 |
| G. Tjon | Automative drinking water monitoring using flow cytometry data | 2019 - 2020 |
| B. Verfaillie | Pattern recognition in raman spectroscopy data for a faster labelling of subjects in multiple domains | 2019 - 2020 |
| K. D'haeyer | Towards a data-driven identification of the microbial "Rammanome" | 2018 - 2019 |
| B. De Clercq | Forecasting tidal surge in the Lower Sea Scheldt using machine learning techniques | 2018 - 2019 |
| G. De Clercq | Deep learning for classification of DNA functional sequences | 2018 - 2019 |
| N. Goeders | Fight the illegal wood trade through chemical fingerprints : The power of mathematics and mass spectrometry | 2018 - 2019 |
| S. Top | Vertical farming of lettuce: the influence of rhizosphere bacteria and substrates | 2018 - 2019 |
| M. Van Haeverbeke | Detection of m6a modifications in native RNA using Oxford Nanopore Technology | 2018 - 2019 |
| A. De Graeve | Detecting climate drivers for vegetation extremes | 2017 - 2018 |
| R. Ingels | Understanding vegetation anomalies with machine learning methods | 2017 - 2018 |
| M. Misonne | Prediction of RNA polymerase-DNA interactions in Escherichia Coli | 2017 - 2018 |
| T. Vanlerberghe | Hierarchical multi-label classification of food products | 2017 - 2018 |
| L. Bodyn | Exploration of deep autoencoders for collaborative filtering on cooking recipes | 2016 - 2017 |
| S. Decubber | Spatiotemporal optimization of Granger causality methods for climate change attribution
| 2016 - 2017 |
| J. Heyse | Development and application of single-cell analysis tools for the study of sympatric bacterial populations | 2016 - 2017 |
| T. Mortier | Modeling of climate-vegetation dynamics using machine learning techniques in a non-linear Granger causality framework | 2016 - 2017 |
| D. Schaumont | Een integratieve benadering gebaseerd op random forest voor de verbeterde predictie van exon-intron juncties | 2016 - 2017 |
| X. Yin | Discovering relationships in climate-vegetation dynamics using dynamic feature selection techniques | 2016 - 2017 |
| C. Zhang | Visualization and unsupervised learning of flow cytometry data for bacterial identication | 2016 - 2017 |
| F. Ramon | A data-driven analysis of ingredients in cooking recipes | 2015 - 2016 |
| L. Tilleman | In silico engineering van cytochroom P450 via machine learning technieken | 2015 - 2016 |
| J. De Reu | Analyse van voorspellingsmodellen voor aardappelziekten en hun toepasbaarheid in Vlaanderen | 2014 - 2015 |
| W.K. Tsang | Assessing pathogen invasion based on community evenness and metabolic similarity | 2014 - 2015 |
| M. De Clercq | Prediction of ingredient combinations using machine learning techniques | 2013 - 2014 |
| A. De Paepe | The prediction of interaction between mRNA and miRNA using machine learning techniques | 2012 - 2013 |
| M. Stock | Learning pairwise relations in bioinformatics: three case studies | 2011 - 2012 |
| J. Vandepitte | Voorspellen van Fusarium spp. aanwezigheid en DON concentraties in wintertarwe met machine learning technieken | 2009 - 2010 |