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@@ -6,68 +6,6 @@ for students and researchers, extensible for research and teaching, as well as e
 and suitable for high-performance computing.
 
 
-## Compiler-based automatic differentiation with Enzyme.jl
-
-**Difficulty**: Medium (up to hard, depending on the chosen subtasks)
-
-**Project size**: 175 hours or 350 hours, depending on the chosen subtasks
-
-[Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) is the Julia frontend of Enzyme, 
-a modern automatic differentiation (AD) framework working at the level of LLVM code. 
-It can provide fast forward and reverse mode AD and - unlike some other AD packages - 
-supports mutating operations. Since [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/) 
-relies on mutating operations and caches for performance, this feature is crucial to obtain
-an implementation that works efficiently for both simulation runs and AD.
-
-The overall goal of this project is to create a working prototype of 
-[Trixi.jl](https://github.com/trixi-framework/Trixi.jl/) (or a subset thereof) 
-using [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) for AD, and to support as 
-many of Trixi's advanced features as possible, such as adaptive mesh refinement, shock capturing etc.
-
-Possible subtasks in this project include
-- Explore and implement forward/backward mode AD via [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) 
-  for a simplified simulation for the 1D advection equation or the 1D compressible Euler equations
-  (e.g., compute the Jacobian of the right-hand side evaluation `Trixi.rhs!` on a simple mesh in
-  serial execution)
-- Explore and implement forward mode AD via
-  [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) of semidiscretizations
-  provided by [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/),
-  mimicking the functionality that is already
-  [available via ForwardDiff.jl](https://trixi-framework.github.io/Trixi.jl/stable/tutorials/differentiable_programming/)
-- Explore and implement reverse mode AD via
-  [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) of semidiscretizations
-  provided by [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/) as required for modern machine learning tasks
-- Explore and implement AD via [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl)
-  of full simulations combining semidiscretizations of
-  [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/) with time integration
-  methods of [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl)
-  
-Related subtasks in this project not related directly to [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl)
-but using other packages include
-- Explore and implement means to improve the current handling of caches to
-  simplify AD and differentiable programming with semidiscretizations of
-  [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/) in general, e.g.,
-  via [PreallocationTools.jl](https://github.com/SciML/PreallocationTools.jl).
-- Extend the current AD support based on [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) 
-  to other functionality of [Trixi.jl](https://github.com/trixi-framework/Trixi.jl/), e.g.,
-  [shock capturing discretizations](https://github.com/trixi-framework/Trixi.jl/issues/1252),
-  [MPI parallel simulations](https://github.com/trixi-framework/Trixi.jl/issues/910),
-  and other [features currently not supported](https://github.com/trixi-framework/Trixi.jl/issues/462)
-
-This project is good for both software engineers interested in the fields of
-numerical analysis and scientific machine learning as well as those students who
-are interested in pursuing graduate research in the field.
-
-**Recommended skills**: Good knowledge of at least one numerical discretization scheme 
-(e.g., finite volume, discontinuous Galerkin, finite differences); initial knowledge 
-in automatic differentiation; preferably the ability (or eagerness to learn) to write fast code
-
-**Expected results**: Contributions to state of the art and production-quality
-automatic differentiation tools for Trixi.jl
-
-**Mentors**: [Hendrik Ranocha](https://github.com/ranocha), [Michael Schlottke-Lakemper](https://github.com/sloede)
-
-
 ## Advanced visualization and in-situ visualization with ParaView
 
 **Difficulty**: Medium