Director of AI/ML and Applied Physics

Department
 

BSD PAT - Raman Lab

About the Department
 

The Pathology Department at the University of Chicago is a department within the Biological Sciences Division focused on understanding the pathogenesis of disease. The Pathology includes clinical faculty, basic science faculty, and a growing contingent of computationally oriented investigators.

The Raman Laboratory is an interdisciplinary lab that uses statistical approaches for addressing biological problems. Current applications include designing synthetic microbiomes for therapeutic purposes as well as in-depth data analysis of high-content biological data obtained from patients. This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Job Summary
 

This person will be broadly in charge of the Artificial Intelligence/Machine Learning (AI/ML) infrastructure as well as applying principles of physics to experimental procedures, protocols, and new experiments for the Raman Laboratory.

This will encompass many different facets of responsibilities. First, this person will build robust infrastructure to facilitate implementing emerging methods of ‘computational learning’ that include, but are not limited to, statistical learning (i.e. LASSO regression, RIDGE regression, learning based upon eigendecomposition (i.e. PCA and SVD), amongst other methods of learning predicated upon linear statistical/physical methods), deep learning (i.e. the use of recurrent neural networks, adversarial neural networks, liquid state machines, reservoir computing, amonst other methods of learning predicated upon non-linear statistical/physical methods), artificial intelligence (i.e. the use of large language models (LLMS), physical computing, and other methods of leraning predicated upon the large parametrization of data to create generative models of statistical data distributions). Second, this person will implement these models in a variety of contexts involving the acquisition of ‘big data’ in the Raman Laboratory. These projects will include creating generative models from (i) multiple sequence alignments of protein sequences spanning ~60 million proteins, (ii) single-cell RNA sequencing data spanning trillions of entries, (iii) spatial transcriptional profiling datasets also spanning trillions of entries, (iv) microbiome data spanning metagenomic and metabolomic data across thousands of clinical samples as well as thousands of synthetic microbial communities, (v) the extant diversity of protein complexes spanning phase-separated condensates found in nature. Third, this person will be in charge of building a robust cloud infrastructure for the Raman Lab to facilitate ease of compute, adequate data storage, and proper data hygiene. Fourth, this person will be in charge of teaching postdoctoral candidates, graduate students, and whoever else within the Raman Lab is interested in learning AI/ML methods that are relevant for addressing their scientific questions of choice per the request of PI Raman. Fifth, this person will be the nucleus of an evolving ‘tech core’ within the Raman Lab, that will be built out over the next three years to modularize the collection, generation, and analysis of ‘big’ biological data. Sixth, this person will be in charge of embracing new methods of data collection and porting them into the lab; teaching graduate students and postdocs how to execute on these new methods of data collection; and creating repositories for ease of use.

Responsibilities

• Executing new AI/ML approaches on large data, training existing personnel within the Raman Laboratory on new AI/ML approaches, integrating new methods of collecting data into the laboratory infrastructure (i.e. requiring wet-lab expertise), creating robust compute infrastructure for the Raman Laboratory.

• Uses specialized depth and breadth of expertise to lead and conduct research experiments.

• Interprets and collects data and trains others in the interpretation of data. Leads the dissemination of data and significantly contributes to scientific publications and grant writing.

• Leads others to solve complex problems, providing expertise to the team which includes of scientists and clinicians from the University and nationally.

• Provides expertise to identify protocol problems, inform investigators of problems, or assist in problem resolution efforts, such as protocol revisions. Provides expertise to evaluate factors such as sample collection processes, data management plans, etc.

• Performs other related work as needed.

Minimum Qualifications
 

Education:

Minimum requirements include a