Current Open Positions in Computational Health Sciences Opportunities at UCSF
Public Health and Computational Biology
The School of Public Health and the Center for Computational Biology in the College of Engineering at the University of California, Berkeley invite applications for a tenure track Assistant Professor position with an expected start date of July 1, 2020. We are interested in candidates working in areas of mutual and complementary interest to the Center for Computational Biology and the School of Public Health. Successful candidates will conduct research, broadly, in the area of computational biology and host/microbe interactions. We are interested in candidates who study how pathogenic or non-pathogenic microbes affect hosts, with a particular interest in studies related to the human microbiome. Eligible candidates for this position would develop and/or apply sophisticated computational and experimental laboratory methods to studies of microbes and their health implications, including the relationship of the human microbiome to chronic or infectious disease at the population level. Candidates applying these techniques to understand health disparities are especially welcome. Examples of areas of high priority research include, but are not limited to, the following:
- Characterization of, including by the use of genome sequencing approaches, human microbes/microbiomes at the community or population level and relationship to health and disease, including disparities in health and disease.
- Computational techniques and methods applied to infectious disease outbreak investigation; the study of pathogenesis, niche adaptation, or evolution of infectious disease agents; and translational applications such as disease diagnostic and biomarker test development, new drug target identification and vaccine development.
- The impact of low-residue antibiotics and other factors in the environment on human intestinal microbiota (e.g., diet, biodiversity, climate change, geography, social determinants).
- Modulation of the human immune response/human physiology by the microbiome.
- Food, agriculture, human nutrition and the impact on gut microbiome.
The position requires a PhD degree or equivalent international degree at the time of application
Accepting applications: September 27, 2019- November 8, 2019
Assistant, Associate, Full Professor
Computational Basic Sciences
The UCSF Bakar Computational Health Sciences Institute (BCHSI) and the Gladstone Institutes are looking to recruit faculty at the Assistant, Associate, or Full Professor level. Candidates must have a strong background in bioinformatics, machine learning, biological networks, and/or mathematical modeling with an interest in biomedical research; they also will have demonstrated significant achievement or promise in their field. Candidates are required to hold a doctorate degree in bioinformatics, computer science, biostatistics, or a related discipline with demonstrated experience/expertise in data science, and/or a medical degree with demonstrated experience/training in data science. A primary affiliation with BCHSI and/or Gladstone Institutes will be provided, along with an academic appointment within the UCSF Department of Epidemiology & Biostatistics or another department closely allied with the applicant’s scholarly expertise. Candidates will be expected to develop extramurally-sponsored research programs and will have opportunities to teach and/or mentor graduate and medical students.
Accepting applications: Oct 15, 2018 – April 10, 2020
Next review date: April 10, 2020
Assistant, Associate, Full Professor
Computational Clinical/Translational Sciences
The Bakar Computational Health Sciences Institute (BCHSI) at UCSF is looking to recruit faculty at the Assistant, Associate, or Full Professor level. BCHSI prefers candidates who have a strong background in clinical informatics, machine learning, and/or network/mathematical modeling with an interest in health research; and to have demonstrated significant achievement or promise in their field. Candidates are required to hold a doctorate degree in informatics, computer science, epidemiology, biostatistics, or a related discipline with demonstrated experience/expertise in informatics, data science, and/or a medical, dental, nursing or pharmacy degree with demonstrated experience/training in informatics or data science. A primary affiliation with BCHSI will be provided, along with an academic appointment within the UCSF Department of Epidemiology & Biostatistics or another department closely allied with the applicant’s scholarly or clinical expertise. Candidates will be expected to teach and/or mentor graduate and medical students while developing extramurally-sponsored research programs.
Accepting applications: Oct 15, 2018 – April 10, 2020
Next review date: April 10, 2020
Arnaout Lab at UCSF
The Arnaout laboratory studies deep learning and other computational methods for biomedical imaging and related clinical data, with the goals of decreasing diagnostic error and developing and scaling novel phenotypes to drive precision medicine. UCSF is a top-10 medical center and a leader in cross-campus efforts to mine, harmonize, and analyze multi-modal clinical data for the University of California’s 15 million patients. The Arnaout laboratory is part of both the Bakar Computational Health Sciences Institute and the nationally ranked Department of Medicine. Projects focus on deep learning for medical imaging, and through collaborative work with intra- and inter-institutional partners, also involve the electronic health record, genetics, and other data types.
See here for more info: Data Scientist Position
Data Science Health Innovation Fellowship
The University of California Berkeley (UC Berkeley) and University of California San Francisco (UCSF) are currently soliciting applications for outstanding data scientists to join a new, groundbreaking data science health innovation fellowship program. Fellows will be provided with 2 years of generous financial support to develop and execute innovative, data-driven research projects in areas of unmet patient needs. Fellows will have access to computer science, engineering, and statistics expertise and technology innovation at UC Berkeley; clinical expertise and data at UCSF; and pharmaceutical industry and translational expertise from Janssen Research & Development. Combining mentorship from all three organizations, large health and biological datasets, and their own data science expertise, Fellows will safely and respectfully conduct research projects with the potential to transform healthcare.
Accepting Applications: June 27- July 29, 2020
Post-Doctoral Fellow Positions at the UCSF Center for Clinical Informatics and Improvement Research (CLIIR)
Overview The newly established Center for Clinical Informatics and Improvement Research (CLIIR) at the University of California San Francisco (UCSF) was created out of a critical need to understand how to use digital tools to improve healthcare quality and value. CLIIR’s mission is to discover ways: (1) to leverage EHRs and other sources of digital data to improve clinical performance and healthcare value, and (2) to scale these results to promote healthcare transformation.
CLIIR is led by Julia Adler-Milstein, PhD, Director of the Center for Clinical Informatics and Improvement Research and Associate Professor at the UCSF Department of Medicine.
CLIIR is recruiting Post-Doctoral Fellows in the field of applied clinical and consumer informatics with interests in health services research and health policy. Post-Doctoral Fellows will be involved in research involving both primary and secondary data collection and analysis.
CLIIR has a specific focus on using clinical and event/audit log data derived from UCSF Health’s EHR to conduct novel research examining both how healthcare is delivered and how informatics tools shape such delivery. The Fellow will use his or her domains of research inquiry along with their quantitative skills, statistical methods and software tools to develop and execute research studies under the mentorship of Dr. Adler-Milstein and other UCSF faculty. The Fellow will be supported by RAs, analysts, and project managers who are part of CLIIR.
The Fellow will be encouraged to take advantage of other training and learning opportunities within the Institute for Computational Health Sciences, Center for Digital Health Innovation, and other related entities at UCSF. We hope that this experience will prepare the applicant for an independent research career in applied health informatics.
- PhD in Health Informatics, Information Science, Computer Science, Health Services Research, or a related discipline.
- Strong programming skills, preferably SQL.
- Strong statistical programming skills in one or more programs (e.g. R, SAS, Stata)
- Database and large dataset experience.
- Good interpersonal and communication skills and a demonstrated commitment to working collaboratively with clinicians and biomedical researchers.
- Ability to work independently.
- Experience with Pattern Recognition, Machine Learning, and/or Natural Language Processing
- Leadership qualities
- Epic Clarity certified and/or experience working with Clarity
- Familiarity with federal health IT policies (e.g., Meaningful Use, MACRA)
How to Apply
Please email Anjali Garg at email@example.com with your:
- Cover Letter including description of research interests and career goals
- Contact Information for 3 References
Opportunities outside of UCSF
Manifest MedEx fellowship
The Manifest MedEx fellowship program is a competitive leadership development opportunity for recent graduates who are interested in new business models, data, quality improvement and innovation in healthcare. The program is open to new graduates with a relevant Master’s degrees (e.g., data science, public health, public policy, business etc) or a Bachelor’s degree and 1-2 years of relevant work experience. MX offers competitive salary and benefits. Manifest MedEx—a nonprofit health network— is helping California health leaders reach their goals of improving outcomes and reducing costs. We’re unlocking information from its silos so California’s doctors, hospitals and health plans can put it to work to improve healthcare.
D2G Oncology is hiring
D2G Oncology is a precision oncology startup company located in Mountain View, California. We are seeking highly motivated, energetic, and creative individuals to join our team.
Cancer genome sequencing efforts have uncovered the spectrum of genomic alterations that occur in human cancers, and clinical cancer genotyping provides a stepping-stone toward personalized therapy. However, a future in which clinicians can provide the most effective therapies requires the knowledge of which drugs will work best for which patients. Thus, predicting how a tumor genotype will affect therapeutic response is an imperative goal within oncology. D2G Oncology’s innovative, scalable, and quantitative approaches will relate drugs to genotypes to transform precision cancer therapeutics.
- Computational biologist / Data scientist
You will have a Ph.D. in a quantitative field (not necessarily biology). Experience with next- generation sequencing analysis, human genomic data analysis, and/or statistical modeling. Experience with python, R, AWS, and/or mySQL is a plus. You will work on exciting and diverse projects while collaborating with diverse groups of people, managing large-scale computational projects, and working with external collaborations. You will have the opportunity to participate in all aspects of research and development within the company, and work in close collaboration with the computational and experimental teams. Position will be commensurate with experience and will have the potential for upward growth.
- Molecular biologist / Cancer biologist
You will have a Ph.D. in biology or related discipline and have expertise in molecular biology. Experience with cancer biology, pharmacogenomics, and/or in vivo cancer models is a plus. You will work on exciting and diverse projects while collaborating with diverse groups of people, managing large-scale experimental projects, and working with external collaborations. You will have the opportunity to participate in all aspects of research and development within the company, and work in close collaboration with the computational and experimental teams. Position will be commensurate with experience and will have the potential for upward growth.
Does this sound interesting? To learn more, please send your CV to info@D2G-oncology.com
Machine Learning Engineer, Informatics & Predictive Sciences
This role will provide collaborative, creative, and interdisciplinary applied machine learning research, development, and operationalization within Celgene’s Informatics & Predictive Sciences department.
- Formulate predictive modeling and learning scenarios and apply cutting-edge machine learning (deep learning) approaches for data-driven decision making
- Develop new approaches to open questions posed by Celgene colleagues of their processes and related datasets
- Operationalize machine learning solutions by collaborating in the development of appropriate infrastructure and interfaces
The successful candidate will provide strong problem-solving skills and hands-on software engineering capability to implement a wide range of data and machine learning solutions.
Scenarios include working alongside experts in familiar applications of machine learning in the biotechnology domain, e.g., patient selection, bioinformatics, cheminformatics, molecular biology, biomedical informatics
Assistant Professorships in Computational Cancer Immunology and Cancer Biology
Seeking candidates who wish to uncover: 1) key mechanisms regulating cancer-associated adaptive and innate immune cells in the tumor microenvironment and identify new immune-oncology therapies, including T cell and B cell repertoire analysis, single cell and deconvolution analysis of myeloid complexity and functional status, and computational modeling of tumor/stromal interactions; and 2) understand the consequences of tumor heterogeneity for tumor evolution, treatment, and outcome, where aspects of tumor biology might be interrogated by computational analysis and integration of data generated from a variety of techniques, include imaging, DNA sequence characterization, and measurements of mRNA or protein.
We seek faculty members with a strong commitment to interdisciplinary collaboration, integrated team science and open science. Candidates should have advanced training in machine learning or statistical learning techniques, with a track record of innovation in a biomedical and/or clinical application, as evidenced by a strong publication record and development of novel methodological approaches.