Career Compass Featured Jobs
Research Fellow - Biology
The Infinity Fellowship
Job description:
We are a frontier artificial-intelligence laboratory, still in stealth, established for a single purpose: to build an AI capable of original discovery — a system that does not merely retrieve and recombine what is already known, but reasons toward genuinely new understanding of the problems put to it. We hold that an instrument able to truly discover is among the most consequential technologies humanity will ever build, and we are building it.
Our system has already produced results beyond the reach of any human team. We have demonstrated its power on a small number of problems. The purpose of this fellowship is to establish how far that power extends.
This is an invitation to bring a small number of researchers who will form the inaugural cohort of the Infinity Fellowship who will spend a year aiming this instrument at the hardest unsolved problems in their fields.
You choose the problems — the ones shelved because they demanded a decade. You carry each from formulation to validated result. You publish as lead author.
The terms are plain: a full-time salaried appointment of roughly one year ($125,000), hybrid in the San Francisco East Bay, with relocation support and visa sponsorship for international Fellows. It pays like a strong postdoc. No background in AI is required; we weigh the ability to learn above experience with any method. Top-performing Fellows will be offered permanent research positions.
The application takes ten minutes to begin: your CV, and a few sentences on the problem you would start with. The full one-page proposal — why it has resisted solution, what its solution would make possible — can follow.
pfa.zto77.com/apply-biology-life-sciences
Proposals received by Sep 15 will be considered first for the inaugural cohort, which convenes in September. Every application receives an answer within a week.
Few are present, in any era, at the founding of a new instrument of discovery. Bring the problem worth your year.
Postdoc in Computational Genomics at Harvard School of Public Health
Harvard University
Environmental Health, Biostatistics
Cambridge, Massachusetts, USA
Job description: The Broad Institute of MIT & Harvard is seeking a highly motivated computational Postdoctoral Associate to join the Haber Lab and Klarman Cell Observatory to discover the complex cellular processes and structures that underlie our airways ability to defend themselves from the wide range of threats in the air around us.
Our interdisciplinary research group uses machine learning and genomics to study the immunology of environmental health and particularly physiological mechanisms of lung inflammation and asthma. We use cutting edge genomics and computational methods to answer major questions in the field: how do the airways of the lungs detect danger signals, such as allergens, viruses, or toxins, and trigger an immune response? Which are the most important types of ‘sentinel cells’ that are on the lookout for danger in inhaled air, and able to co-ordinate an effective defense? Prospective applicants should have made significant contributions to their area of study, as evidenced by a strong publication record. We are seeking creative and highly motivated individuals who want to work in a dynamic, multi-disciplinary research environment.
Qualifications
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PhD or equivalent in computational biology, machine learning, data science, artificial intelligence, computer science, statistics, mathematics, or another quantitative field.
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Candidates holding a degree in biological or medical science are also welcome to apply if they have strong background in computational or statistical work.
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Strong interpersonal and communication skills, experience presenting and communicating research.
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Excellent problem solving and quantitative analysis skills
Faculty Member- Computational Biology, Machine Learning, & Digital Health
Institute for Systems Biology
Seattle, Washington, USA
Job description: The Institute for Systems Biology (ISB) seeks a highly motivated and
talented Assistant, Associate, or Full Professor to join our faculty and lead transformative efforts in AI, machine learning, and computational biology to bridge the gap between basic science and next-generation medicine. The successful candidate will have a strong background in computational biology, statistics, machine learning/AI or a related field, and a passion for applying these techniques to address complex biological or medical problems.
Biomedical research pioneer Dr. Leroy Hood founded ISB in 2000 as the first-of-its-kind institute dedicated to systems biology. Unlike traditional academic departments, our faculty are cross-disciplinary, which enables us to take on big, complex problems. ISB is deeply committed to sharing our knowledge with society: we participate in the formation of companies, and strategically collaborate with healthcare systems and industry to improve human health and accelerate science.
ISB’s computational capabilities are in biological modeling and simulation, machine learning, biostatistical methods, bioinformatics, medical informatics, applied to: genomics, phenomics and exposomics. ISB's technical capacities include genomics, transcriptomics, metagenomics, proteomics, metabolomics, imaging, and single-cell omics. Accordingly, ISB faculty develop novel measurement technologies, formal theories and computational tools to support all aspects of systems biology and systems medicine, and these unique resources are shared across the institute. ISB’s affiliation with Providence, one of the largest not-for-profit health care systems in the United States, gives ISB access to unique resources including clinical trials, patient samples, 30 million electronic health records (EHRs), imaging, and the ability to translate discoveries into clinical applications that benefit patients.
Responsibilities:
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Establish and maintain a research program in applied mathematics, statistics, computer science or data science to investigate fundamental biological processes and their relevance to human health.
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Pioneer innovative computational frameworks to integrate multimodal biological and clinical data, such as genomics, proteomics, phenomics, or specialized domains, such as deep immunophenotyping, imaging, health dynamics, and spatiotemporalomics data that can drive new scientific paradigms or personalized medicine with actionable insights for patient care.
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Develop and apply innovative methods to integrate data-driven and knowledge-based approaches for discovery of mechanisms and clinical decision support.
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Collaborate with other researchers within the institute, external partners to translate computational discoveries into real-world applications in health care, from prevention to detection to treatment of diseases.
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Secure external funding to support research activities and expand the scope of the research program.
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Mentor graduate students, postdoctoral fellows, and undergraduate students.
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Contribute to ISB's research and educational mission.
Expected Salary: Compensation for this role will be based on skills, experience, and rank upon hire in the expected range of $155,000 to $300,000 per year.
Postdoctoral Associate
Yale University
Department of Biostatistics
New Haven, Connecticut, USA
Job description: Leying Guan is an Associate Professor of Biostatistics at Yale Univ. Her team develops statistical and machine-learning methods for reliable scientific discovery from complex, high-dimensional data, along three interconnected directions: (1) models that capture structure in heterogeneous, high-dimensional data — latent-factor and multi-view models, structured/tensor estimation, high-dimensional regression, and integrative modeling; (2) inference that stays valid after complex or data-adaptive analysis — conformal prediction, robust inference, adaptive signal detection with error-rate control, and post-selection assessment; and (3) computational biology — building tools with improved statistical rigor for large-scale immune, single-cell, and multi-omics data, in collaboration with immunologists and consortium-scale data resources. Looking ahead, she is currently extending all three directions further toward new model structures and data types.
Depending on background and interest, projects may span new computational models for single-cell data targeting unresolved challenges; software and computational platform development with large-scale application to consortium data resources; theoretical and methodological development in robust uncertainty quantification or high-dimensional structured modeling. Candidates also have good flexibility to propose and develop their own research topics.
You don't need to fit all of these, but should be strong on at least one axis:
Methodologists: rigorous training in statistics, biostatistics, ML theory, comfortable enough with code and real data to implement and validate methods at scale.
Computational biologists / strong engineers moving into methodology: excellent software and data engineering skills (scalable computing, single-cell or genomic data pipelines).
ML-trained researchers curious about biology: strong deep learning background who are interested in working on problems with real statistical stakes in a biological domain and are motivated to learn the biology as they go.
Across all profiles, what matters most: genuine intellectual motivation, comfort working hands-on with real and large-scale data, and interest in building methods that are both theoretically sound and practically useful.
Compensation
Salary: Compensation will follow Yale University’s postdoctoral compensation guidelines and will be commensurate with experience.
Preferred Start Date: Preferred Early 2027, but negotiable. Applications will be considered until the position is filled.
International applicants are welcome.
Qualifications
PhD (or near completion) in Statistics, Computer Science, Biostatistics, Computational Biology, or a related quantitative field, with a track record in either rigorous methodology, computational software development, or applying/extending computational methods to real, large-scale single cell/omics data.
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