Computational Scientist - Machine Learning and Computer Vision

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

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.

The 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 computational tools for understanding tissue function, including analysis of multi-omics and spatial assays where techniques of compute vision and deep learning are highly pertinent.

What You Will Contribute To Altos

Scientist will be expected to lead development and application of computational methods for analysis of spatial omics data together with more traditional microscopy images, in the context of biological studies of different diseases, aging and other processes impacting tissue homeostasis. Particular emphasis will be placed on analysis of tissue architectures, deciphering cell communications, and control of proliferation within tissues. 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

Minimum Qualifications

• Expert knowledge deep learning methods and computer vision
• Experience in developing tools in python, including modern machine learning frameworks (TensorFlow, PyTorch, JAX)
• PhD. in Computer Science or a related discipline
• Experience working at the intersection of computation and biology
• Track record publications in peer-reviewed journals or conferences
• An interest in carrying out genomics research in collaborative settings

Preferred Qualifications

• Experience in analysis of microscopy or spatial omics data
• Expertise in a large subset of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi-task learning, graph neural networks, active learning, hybrid mechanistic/ML models
• Expertise in computational infrastructure for deep learning including GPUs, TPUs, cloud based machine learning
• Working knowledge of R
• Experience in developing tools in python, including modern machine learning frameworks (TensorFlow, PyTorch, JAX)
• Familiarity with multi-omic integration or spatial transcriptomics analysis