diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
index 16a7f523192c7c591f0bd77927d13d5f2a678d54..bcebd84b419ba40547abc10ae76c15cf76383cfd 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
@@ -62,8 +62,8 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
 Therefore, using nodes with built-in GPUs, e.g., clusters
-[`Capella`](../jobs_and_resources/capella.md),
-[`Alpha`](../jobs_and_resources/alpha_centauri.md) and
+[`Capella`](../jobs_and_resources/hardware_overview.md#capella),
+[`Alpha`](../jobs_and_resources/hardware_overview.md#alpha_centauri) and
 [`Power9`](../jobs_and_resources/hardware_overview.md) is beneficial for the examples here.
 
 ### R Interface to TensorFlow