美国耶鲁大学招聘博士后—生物物理学与计算生物学

美国耶鲁大学招聘博士后—生物物理学与计算生物学

Postdoctoral Fellowship in Biophysics and Computational Biology

Yale University

Date Posted: Posted on 22 July 2025

Location: United States (US)

·         Postdoctoral Position in Computational Biology/Machine Learning

·         Department of Biomedical Engineering

·         We are looking for a postdoctoral researcher to join our team of biophysicists. Our lab is working on two distinct areas: (1) deciphering non-equilibrium strategies employed by signaling networks to perform non-trivial computations (Goetz et al. to appear in eLife and Goetz and Dixit,in review, both manuscripts are on biorxiv) and (2) developing principled latent variable models for microbiomes and their environments (Plata et al. mSystems, 2025). Broadly, we develop machine learning methods that integrate mechanistic and biophysical models with -omics data to discover new biology. The postdoctoral fellowship is funded by Yale Endowed Postdoctoral Fellowships in the Biological Sciences for the first year. The continuation will be funded by the NIH Maximizing Investigator’s Research Award (MIRA).

·         The position offers a competitive salary (70,000$ per year) and generous benefits. We have allotted 5000$ per year discretionary funds, to be used towards expenses such as conference travel and equipment.

·         Your background: Candidates with experience in modeling and analysis of –omics data using mechanistic and biophysical models are strongly preferred. We welcome experimental biophysicists with a quantitative background who are eager to learn computational and machine learning tools. Applications should include a statement detailing research experience, interest, and future plans. The applicant should also include a CV and contact details of at least two letter writers. Applications should be sent directly to Prof. Purushottam Dixit ([Please click the Apply button for the link or address]).

·         Research Focus: A significant component of our research is in building machine learning tools that allow us to incorporate mechanistic models with high dimensional –omics data to gain a deeper knowledge of biological variability. Using those tools, we focus on understanding the biochemical origins of population heterogeneity in signaling networks and its effect on cellular phenotypes (Lyashenko, Niepel, Dixit, et al., eLife, 2020, Dixit et al. Cell Systems, 2020, Goetz et al, eLife, 2023, 2025). With our industry collaborators at BiomEdit LLC, we are interested in modeling the effects of environmental perturbations on host-associated microbial ecosystems (Zhao et al. PLoS Comp. Bio., 2021, Plata et al. mSystems, 2025). We also have an experimental collaboration with the labs of Laurence Morel in the Department of Microbiology, Immunology, and Molecular Genetics at UT Health, San Antonio where we are investigating metabolism of T cells in the context of autoimmunity (Goetz et al., Front. Immun. 2024). There will be plenty of opportunities to collaborate with labs at Yale.

·         About the university: Yale boasts world class research programs in several scientific fields. Accessible via public transport from both New York and Boston, Yale is well connected with world class institutes in the northeast corridor. Our lab will be situated at the main campus and will have a presence at the Systems Biology Institute at the West Campus.

·         Group ethics: Science is practiced by a community of individuals with diverse identities and is welcoming to everyone. We recognize the value in diversity, equity, and inclusion in all aspects of our research. This includes, but is not limited to differences in race, ethnicity, gender and sexual identity, age, socioeconomic status, religion, and disability. Science, like all human endeavors, is learned. We strive to foster an atmosphere of learning that is based on inclusion, transparency, and respect for all group members.

·         Tagged as: Data Science, Life Sciences

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