diff --git a/intern_program/intern_program_fall_2018_projects.md b/intern_program/intern_program_fall_2018_projects.md
index fc6dc76..d44ae61 100644
--- a/intern_program/intern_program_fall_2018_projects.md
+++ b/intern_program/intern_program_fall_2018_projects.md
@@ -111,4 +111,15 @@ Felix Waldhauser, Lamont Research Professor
 This project is to apply novel machine learning methods for characterization of earthquakes. Real time monitoring of major faults (like the San Andreas Fault, CA) focuses on detection and location of small earthquakes (microseismicity). The scientific holy grail of identification of precursors to large earthquakes may lie in recognizing changes in patterns of microseismicity. Our new unsupervised machine learning methods analyze subtle patterns in the frequency content of microseismic events that probably tell us about the mechanical state of the fault. This project consists of applying these methods to past microseismicity on the San Andreas Fault, to look for patterns in spectral character, towards the development of new real time fault monitoring tools. Our methods involve three main sequential steps: (1) Non-negative matrix factorization (NMF), (2) Hidden Markov models (HMM) and (3) k-means clustering. NMF and HMM are unsupervised feature extraction and dimensionality reduction; k-means is unsupervised clustering.
 
 #### Skills Required
-Familiarity with Python. Interest in signal processing, machine learning and earthquake physics.
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+Familiarity with Python. Interest in signal processing, machine learning and earthquake physics.
+
+### PyIngrid: a simple Python interface for calculation of climate anomalies using the IRI Data Library
+
+#### Supervisors
+[Andrew W. Robertson](https://https://iri.columbia.edu/contact/staff-directory/andrew-robertson/), Head of Climate Group/Senior Research Scientist at the [IRI](https://iri.columbia.edu); [Ángel G. Muñoz] (https://iri.columbia.edu/contact/staff-directory/angel-munoz/), Associate Research Scientist at the [IRI](https://iri.columbia.edu)
+
+#### Description
+We are looking for an enthusiastic Pythonizer to help us advance a simple Python interface already in development, which connects to the IRI Data Library (DL) and uses its coding language, called Ingrid, to compute climatologies and climate anomalies for sub-seasonal (~15-45 days) forecasts. The goal of this particular internship is to write a Python dictionary  to facilitate this interface. The outcome of this internship will be directly used by PyCPT, a Python interface for the Climate Predictability Tool, widely used for research and operational forecasts around the world.   
+
+#### Skills Required
+Basic Python programming, willingness to learn Ingrid (DL's programming language)