I recently posted a DeepChem Model Wishlist which listed a series of scientific machine learning models that we’d like to see implemented in DeepChem and invited contributions from the community. The post and tweet raised a lot of interest from the community, suggesting that there is a need for high quality standardized implementations of scientific machine learning models.
For this reason, we are pleased to announce that Deep Forest Sciences is partnering with CMU under the auspices of the ARPA-E ACED-Differentiate program led by Professor Venkat Viswanathan to offer funding for junior scientists to add these models into DeepChem. The positions will be structured as part time internships, for about 20 hrs/week for about 3 months. We are flexible with time commitment and duration. Suitable applicants should have a background in machine learning or scientific computing and ideally experience in scientific machine learning research. Preference will be given to US based students but exceptional international students will be considered as well. Over the course of this part time internship, DeepChem developers will teach students how to take research code for scientific machine learning and turn it into industrial strength implementations that can be used widely by the community. We encourage researchers in particular to apply to implement techniques that they’ve designed since incorporating a method into an open source framework like DeepChem will increase citations and long-term impact.