This page lists a bunch of project ideas, meant to serve as starting points as you develop your own plan. You're free to modify them to your tastes, or propose something totally different in an area you're interested in.
We have our project ideas organized below roughly by domain but you can also see a single page with all our projects together:
BayesianOptimization.jl - a package for global optimization of black-box functions
Compiler – work on the Julia compiler's internals to make things better for everyone.
DeepChem.jl – Machine learning for atomic systems in Julia
DFTK.jl – Density-functional theory in Julia
Documentation tooling - Tooling related to documentation generation, docstrings etc.
Loop Optimization - Loop analysis and optimization
Flux.jl - A flexible deep learning library.
GeoStats.jl - An extensible framework for high-performance geostatistics in Julia.
High Performance and Parallel Computing – write code that runs on lots of machines, goes really fast, processes lots of data, or all three.
Images – extend Julia's suite of tools for visualization and analysis of images.
Javis - Julia animations and visualizations for teaching, communication, and beyond!
JuliaDynamics - Dynamical systems, complex systems and nonlinear dynamics in Julia
MLJ.jl – a Machine Learning Toolbox for Julia.
Numerics – Challenges for the hard–core number-cruncher, including linear algebra routines and basic mathematical functions.
Pluto - Pluto notebooks and tools for teaching
Pythia - scalable time series for Julia.
Signal processing - Continuous time Signal Processing
Symbolic computation - User friendly symbolic programming
Tabular Data - Working with data
TableTransforms.jl - Tabular transforms used in statistics and machine learning
Turing - for probabilistic modelling and probabilistic programming
VS Code - Improving Julia's VS Code IDE experience
We also have Julia project's available under other organizations. If you are applying for those projects, make sure your application is for that organization and NOT the Julia Language:
SciML - Scientific Machine Learning
Differential Equations - Numerical methods for high-performance solving of differential equation models.