We believe that diverse perspectives are foundational to scientific innovation and inquiry.
We are building a company where exceptional scientists and industry leaders from around the world work side by side to advance a shared mission.
Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives.
At Altos, we are all accountable for sustaining a diverse and inclusive environment.
Altos Labs aims to foster scientific creativity, providing research labs with both resources and freedom needed to pursue fundamental scientific challenges, including the ability of individual labs to publish and present their work for wider scientific community.
Kharchenko lab at Altos Labs San Diego Institute is studying how cells coordinate their activity within complex biological tissues, how these mechanisms break down in disease and injury, and the potential interventions that may improve tissue function. Much of the effort is focused on development and application of novel statistical methods and computational tools for understanding tissue function, including analysis of multi-omics and spatial assays.
What You Will Contribute To Altos
Scientist will be expected to lead development and application of computational methods for analysis of single-cell multi-omics and spatial omics data, in the context of biological studies of different diseases, aging and other processes impacting tissue homeostasis. Particular emphasis will be placed on deciphering cell communications, control of proliferation within tissues, as well as epigenetic regulation of individual cells. Successful candidate will collaborate with both internal and external experimental groups, lead or participate in planning experimental designs, author and contribute to biological and methodological manuscripts, contribute to seminars and other scientific initiations within Altos as well as wider scientific community.
Who You Are
PhD in Computational Biology, Computer Science or a related discipline
Track record publications in peer-reviewed journals
A strong interest in conducting genomics research in a collaborative setting
Knowledge of statistical methods, probabilistic modeling techniques, machine learning methods
Track record of analysis of genomic data, including RNA, chromatin and DNA modifications, proteins
Knowledge of single-cell analysis methods
Experience in developing tools in R or python, experience with relevant repositories
Working knowledge of deep learning techniques, variational optimization, generative models
Experience in image analysis and related machine learning methods
Familiarity with multi-omic integration or spatial transcriptomics analysis