  An algorithm for the identification of jets that originate from the
hadronisation of strange quarks is presented, which complements existing
algorithms for the identification of jets that originate from $b$-quarks and
$c$-quarks. The algorithm is based on the properties of tracks and uses long
short-term memory recurrent neural networks to discriminate between jets from
strange quarks and jets from down and up quarks. The performance of the
algorithm is compared to a simple benchmark algorithm that uses the
transverse-momentum fraction carried by a reconstructed $K_S \rightarrow
\pi^+\pi^-$ decay. While the benchmark algorithm is limited to signal
efficiencies smaller than 13%, the proposed algorithm is not limited in
efficiency. For signal efficiencies of 30% and 70%, background efficiencies of
21% and 63% are achieved, indicating the challenge of discriminating strange
jets from jets that originate from first-generation quarks.
