Andrew MacDougall researches nature-based climate solutions, using ACENET to run complex models. His work explores carbon absorption, potential warming effects, and the impact of reaching net-zero emissions.

David Joly researches cannabis disease resistance by studying plant genes and pathogen interactions. ACENET provides crucial bioinformatics tools and training for his team, accelerating genomic analysis.

Uyen Lai researches how to make generative AI text sound more human. Her work, using ACENET resources, compares AI and human writing styles to improve content creation and realism.

Ian Bradbury uses DNA technology and machine learning to predict how climate change will impact Eastern Canada’s aquatic species, like Arctic char. ACENET provides the computational power needed to analyze massive genetic datasets.

Dr. Damjanov investigates how galaxies evolve, focusing on the interplay between their size, stellar content, and environment. Her team uses ACENET to analyze massive datasets from telescopes and simulations.

Lourdes Peña-Castillo uses computational microbiology and machine learning to understand bacterial genomes. Her goal: to strategically manipulate bacteria for targeted treatments, rather than broad-spectrum antibiotics, enabling 'smart biotechnology'.

Stijn De Baerdemacker uses computational chemistry to simulate molecular behavior, accelerating the discovery of new materials and drugs. ACENET’s resources speed up this process by many orders of magnitude.

Samira Barmaki uses laser pulses and high-performance computing to explore the behavior of electrons in atoms and molecules. Her simulations aid experimental studies and train the next generation of computational physicists.

Catherine Lovekin uses asteroseismology – measuring star variations – to study convective overshoot and understand stellar interiors. ACENET’s high-performance computing is crucial for her complex models and student research.

Erin Mazerolle is developing software to improve reproducibility in brain imaging analysis, particularly for neurological diseases like Alzheimer’s. ACENET’s computing power makes this complex 'multiverse analysis' feasible.