Postdoctoral Positions in the Lab of Isaac Kohane ("Zaklab")
Areas of focus
- Large Language Models (LLM)
- Single Cell Transcriptomics/Aging
- AI to automate patient diagnosis
- Cancer treatment response
Large Language Models (LLM)
Summary
We are seeking a highly motivated and skilled Postdoctoral Fellow to join the lab of Dr. Isaac Kohane at Harvard Medical School Department for Biomedical Informatics. This position offers an exciting opportunity to contribute to cutting-edge research in medical text processing using Large Language Models (LLM). The successful candidate will play a pivotal role in advancing our understanding of medical text analysis and its applications in healthcare.
Job Responsibilities
- Conduct research and development activities using Large Language Models (LLM) for medical text processing.
- Collaborate with a multidisciplinary team of researchers to design and implement novel algorithms and models for medical text analysis.
- Analyze large-scale medical datasets, extract relevant information, and develop innovative approaches to improve the accuracy and efficiency of medical text processing.
- Evaluate and validate the performance of developed algorithms and models through rigorous experimentation and benchmarking.
- Contribute to the publication of research findings in high-impact scientific journals and present research outcomes at conferences and seminars.
- Assist in mentoring and supervising researches in our lab, providing guidance and support in their research projects related to medical text processing.
Requirements
- Ph.D. in Bioinformatics, Computational Linguistics, or a related field with a track record of publishing high-quality research papers in the field.
- Strong expertise and hands-on experience in using LLMs for medical text processing.
- Background in medicine or a related healthcare field is highly desirable.
- Proficient programming skills in Python and R or another relevant programming language commonly used in data science and natural language processing.
- Experience with large-scale medical datasets and familiarity with biomedical ontologies and terminologies.
Apply
To apply, please submit a cover letter, detailed curriculum vitae, a list of references, and copies of your most significant publications or preprints to p2@zaklab.org.
Single Cell Transcriptomics/Aging
Summary
We are seeking a highly motivated and skilled Postdoctoral Researcher to join the labs of Dr. Isaac Kohane and Dr. Chirag Patel at Harvard Medical School Department for Biomedical Informatics. This position offers an exciting opportunity to contribute to cutting-edge research in single-cell transcriptomics with a specific focus on aging. The successful candidate will work collaboratively with Dr. Kohane's lab, Dr. Chirag Patel's lab, and Dr. Lee Rubin's lab at Harvard's Stem Cell and Regenerative Biology Department. This collaborative effort aims to advance our understanding of the molecular mechanisms underlying aging and age-related diseases using state-of-the-art single cell transcriptomic technologies.
Job Responsibilities
- Conduct experiments in the field of single cell transcriptomics to investigate the molecular changes associated with aging and age-related diseases.
- Perform data analysis and interpretation of single cell transcriptomic datasets, including data preprocessing, quality control, differential expression analysis, and identification of cell subtypes and their gene expression profiles.
- Collaborate closely with Dr. Isaac Kohane's lab, Dr. Chirag Patel's lab, and Dr. Lee Rubin's lab to integrate single cell transcriptomic data with other omics data types, such as genomics, epigenomics, and proteomics, to gain comprehensive insights into the aging process.
- Contribute to the development and optimization of novel single cell transcriptomic methodologies and technologies for studying aging and age-related diseases.
- Work on the application of computational and statistical approaches, including machine learning algorithms, for the analysis and interpretation of single cell transcriptomic data.
- Publish research findings in high-impact scientific journals and present research outcomes at conferences and seminars.
- Collaborate with other members of the research teams, including graduate students and technicians, providing guidance and support in their research projects related to single cell transcriptomics and aging.
- Stay up-to-date with the latest advancements in single cell transcriptomics, aging biology, and computational approaches, actively participating in scientific discussions and journal clubs.
Requirements
- Ph.D. in a relevant field, such as Computational Biology, Bioinformatics, or a related discipline with a track record of publishing high-quality research papers in the field.
- Strong expertise and hands-on experience in single cell transcriptomics data including preprocessing pipelines and downstream analyses.
- Research background in the biology of aging and age-related diseases.
- Proficiency in R or Python.
- Experience with computational and statistical approaches, including machine learning algorithms, for analyzing biological data.
- Familiarity with integrating multi-omics datasets and applying computational approaches for integrative analysis is highly desirable.
Apply
To apply, please submit a cover letter, detailed curriculum vitae, a list of references, and copies of your most significant publications or preprints to p1@zaklab.org.
AI to automate patient diagnosis
Summary
Join a project to automate diagnosis using artificial intelligence so that both patients and doctors can improve the accuracy and clinical value of diagnostic decision-making. Be a member of a multidisciplinary biocomputing team and contribute to a public-facing project.
Requirements
- Expertise in machine learning required.
- Familiarity with embeddings helpful but not required.
Apply
Send email to admin@zaklab.org.
Cancer treatment response
Summary
Rare individuals with aggressive cancers will have long-term lasting response to therapy even when most do not. We are trying to understand why. DBMI Professor and Chair Isaac Kohane seeks a postdoctoral fellow to lead the genomic analyses of these individuals (including somatic and germline genome). In the course of the analyses you will work with a small team, including scientists in the pharmaceutical industry, that will help integrate other analyses of these individuals.
Requirements
- Experience with NGS bioinformatics analyses
- Experience with R or Python scripting
Apply
Send email to admin@zaklab.org
Harvard Medical School is an Equal Opportunity/Affirmative Action Employer.
Women and minorities are especially encouraged to apply.
