-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
for analyzing data sets and experience working with BIG DATA (eg. NGS data in multi-TB scale). Experience using genome alignment software (bowtie2, bwa, tophat, etc.) is desired. Fluent in one programming
-
with BIG DATA (eg. NGS data in multi-TB scale). Experience using genome alignment software (bowtie2, bwa, tophat, etc.) is desired. Fluent in one programming language (Python, C, C++ or Java) and
-
to analyze large multi-modal datasets and/or who wish to deploy the next generation of exposome AI models. These positions come with data ready to analyze: the candidate can focus on developing research
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
-
cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
-
focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute