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HomeCompaniesAa067 Sci Rsrch EnPostdoctoral Fellow - Modeling Tumor Evolution and Treatment (Hybrid)

Postdoctoral Fellow - Modeling Tumor Evolution and Treatment (Hybrid)

Aa067 Sci Rsrch En · United States-California-Duarte · Active · Oracle Taleo Enterprise

Job facts

FieldValue
CompanyAa067 Sci Rsrch En
TitlePostdoctoral Fellow - Modeling Tumor Evolution and Treatment (Hybrid)
Normalized title-
Department / teamDays
LocationDuarte, CA, United States
Work model-
Employment type-
Salary-
Statusactive
ATS providerOracle Taleo Enterprise
Posted / first seen / 2026-05-31
Changed / last seen2026-06-05 / 2026-06-06

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Linked records

CompanyAa067 Sci Rsrch En
Sourcef78e7b35-939f-4bec-9d27-0fe8f2343d0b
ATS providerOracle Taleo Enterprise

Description

Postdoctoral Research Fellow — Modeling Tumor Evolution and Treatment Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research. The Bild Laboratory at City of Hope uses systems biology to understand how tumors evolve under therapy, uncover resistance mechanisms, and identify actionable vulnerabilities. We integrate longitudinal patient cohorts with single-cell and bulk multi-omics, liquid biopsy, and patient-derived models, partnering closely with clinicians at an NCI-designated Comprehensive Cancer Center. We are seeking a Postdoctoral Research Fellow to lead computational projects at the interface of tumor evolution, liquid biopsy, and machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints to model tumor population dynamics during treatment and predict clinical outcomes. This is a highly collaborative, translational role for a scientist who wants to connect methods development with impactful questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning frameworks that integrate multimodal data to predict therapeutic response. Construct scalable pipelines for analyzing large longitudinal genomic cohorts. Validate computationally derived biomarkers in collaboration with experimental and clinical teams. Publish in high-impact journals and present at major conferences. Mentor junior lab members. Develop an independent research direction that positions you for a faculty or senior industry role. Postdoctoral Research Fellow — Modeling Tumor Evolution and Treatment Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research. The Bild Laboratory at City of Hope uses systems biology to understand how tumors evolve under therapy, uncover resistance mechanisms, and identify actionable vulnerabilities. We integrate longitudinal patient cohorts with single-cell and bulk multi-omics, liquid biopsy, and patient-derived models, partnering closely with clinicians at an NCI-designated Comprehensive Cancer Center. We are seeking a Postdoctoral Research Fellow to lead computational projects at the interface of tumor evolution, liquid biopsy, and machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints to model tumor population dynamics during treatment and predict clinical outcomes. This is a highly collaborative, translational role for a scientist who wants to connect methods development with impactful questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning frameworks that integrate multimodal data to predict therapeutic response. Construct scalable pipelines for analyzing large longitudinal genomic cohorts. Validate computationally derived biomarkers in collaboration with experimental and clinical teams. Publish in high-impact journals and present at major conferences. Mentor junior lab members. Develop an independent research direction that positions you for a faculty or senior industry role. Your qualifications should include: A PhD (or equivalent) in computational biology, bioinformatics, systems biology, biomedical engineering, statistics, computer science, or a closely related quantitative field. Demonstrated expertise across most of the following areas: Cancer biology domain knowledge Working understanding of tumor evolution and clonal dynamics, pathway and signaling biology in the context of the hallmarks of cancer, and the molecular biology connecting DNA mutation and methylation to RNA and protein function. Familiarity with liquid biopsy modalities (ctDNA, cfDNA methylation, CTCs) and their clinical applications, along with awareness of oncology biomarker validation frameworks, is strongly preferred. Computational and machine learning skills Proficiency in large-scale genomic data management and bioinformatic processing, including fluency with R/Bioconductor workflows and Python scientific stacks. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, and ideally with probabilistic programming tools such as Pyro or Stan. Experience with multimodal data fusion and building efficient, scalable data pipelines for large genomic datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly record and collaboration A track record of first-author peer-reviewed publications (or preprints) appropriate to career stage, and the communication skills to work effectively across a team that spans multiple disciplines. City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is an equal opportunity employer. To learn more about our Comprehensive Benefits, please CLICK HERE . #PD Your qualifications should include: A PhD (or equivalent) in computational biology, bioinformatics, systems biology, biomedical engineering, statistics, computer science, or a closely related quantitative field. Demonstrated expertise across most of the following areas: Cancer biology domain knowledge Working understanding of tumor evolution and clonal dynamics, pathway and signaling biology in the context of the hallmarks of cancer, and the molecular biology connecting DNA mutation and methylation to RNA and protein function. Familiarity with liquid biopsy modalities (ctDNA, cfDNA methylation, CTCs) and their clinical applications, along with awareness of oncology biomarker validation frameworks, is strongly preferred. Computational and machine learning skills Proficiency in large-scale genomic data management and bioinformatic processing, including fluency with R/Bioconductor workflows and Python scientific stacks. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, and ideally with probabilistic programming tools such as Pyro or Stan. Experience with multimodal data fusion and building efficient, scalable data pipelines for large genomic datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly record and collaboration A track record of first-author peer-reviewed publications (or preprints) appropriate to career stage, and the communication skills to work effectively across a team that spans multiple disciplines. City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is an equal opportunity employer. To learn more about our Comprehensive Benefits, please CLICK HERE . #PD

Full job record

Job IDe3e97dd67b97433e888666b8c5af962c79df4757
Org ID23a7cd11-5129-4d0d-8086-8a34956f4b37
Source IDf78e7b35-939f-4bec-9d27-0fe8f2343d0b
Board IDf78e7b35-939f-4bec-9d27-0fe8f2343d0b
Provideroracle_taleo
Provider Job Key239445
TitlePostdoctoral Fellow - Modeling Tumor Evolution and Treatment (Hybrid)
Normalized Title
Statusactive
Activeyes
Location TextUnited States-California-Duarte
DepartmentDays
Team
Employment Type
Workplace Type
Remote Policy
CountryUnited States
RegionCA
CityDuarte
Salary RawPostdoctoral Research Fellow — Modeling Tumor Evolution and Treatment Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research. The Bild Laboratory at City of Hope uses systems biology to understand how tumors evolve under therapy, uncover resistance mechanisms, and identify actionable vulnerabilities. We integrate longitudinal patient cohorts with single-cell and bulk multi-omics, liquid biopsy, and patient-derived models, partnering closely with clinicians at an NCI-designated Comprehensive Cancer Center. We are seeking a Postdoctoral Research Fellow to lead computational projects at the interface of tumor evolution, liquid biopsy, and machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints to model tumor population dynamics during treatment and predict clinical outcomes. This is a highly collaborative, translational role for a scientist who wants to connect methods development with impactful questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning frameworks that integrate multimodal data to predict therapeutic response. Construct scalable pipelines for analyzing large longitudinal genomic cohorts. Validate computationally derived biomarkers in collaboration with experimental and clinical teams. Publish in high-impact journals and present at major conferences. Mentor junior lab members. Develop an independent research direction that positions you for a faculty or senior industry role. Postdoctoral Research Fellow — Modeling Tumor Evolution and Treatment Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research. The Bild Laboratory at City of Hope uses systems biology to understand how tumors evolve under therapy, uncover resistance mechanisms, and identify actionable vulnerabilities. We integrate longitudinal patient cohorts with single-cell and bulk multi-omics, liquid biopsy, and patient-derived models, partnering closely with clinicians at an NCI-designated Comprehensive Cancer Center. We are seeking a Postdoctoral Research Fellow to lead computational projects at the interface of tumor evolution, liquid biopsy, and machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints to model tumor population dynamics during treatment and predict clinical outcomes. This is a highly collaborative, translational role for a scientist who wants to connect methods development with impactful questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning frameworks that integrate multimodal data to predict therapeutic response. Construct scalable pipelines for analyzing large longitudinal genomic cohorts. Validate computationally derived biomarkers in collaboration with experimental and clinical teams. Publish in high-impact journals and present at major conferences. Mentor junior lab members. Develop an independent research direction that positions you for a faculty or senior industry role. Your qualifications should include: A PhD (or equivalent) in computational biology, bioinformatics, systems biology, biomedical engineering, statistics, computer science, or a closely related quantitative field. Demonstrated expertise across most of the following areas: Cancer biology domain knowledge Working understanding of tumor evolution and clonal dynamics, pathway and signaling biology in the context of the hallmarks of cancer, and the molecular biology connecting DNA mutation and methylation to RNA and protein function. Familiarity with liquid biopsy modalities (ctDNA, cfDNA methylation, CTCs) and their clinical applications, along with awareness of oncology biomarker validation frameworks, is strongly preferred. Computational and machine learning skills Proficiency in large-scale genomic data management and bioinformatic processing, including fluency with R/Bioconductor workflows and Python scientific stacks. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, and ideally with probabilistic programming tools such as Pyro or Stan. Experience with multimodal data fusion and building efficient, scalable data pipelines for large genomic datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly record and collaboration A track record of first-author peer-reviewed publications (or preprints) appropriate to career stage, and the communication skills to work effectively across a team that spans multiple disciplines. City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is an equal opportunity employer. To learn more about our Comprehensive Benefits, please CLICK HERE . #PD Your qualifications should include: A PhD (or equivalent) in computational biology, bioinformatics, systems biology, biomedical engineering, statistics, computer science, or a closely related quantitative field. Demonstrated expertise across most of the following areas: Cancer biology domain knowledge Working understanding of tumor evolution and clonal dynamics, pathway and signaling biology in the context of the hallmarks of cancer, and the molecular biology connecting DNA mutation and methylation to RNA and protein function. Familiarity with liquid biopsy modalities (ctDNA, cfDNA methylation, CTCs) and their clinical applications, along with awareness of oncology biomarker validation frameworks, is strongly preferred. Computational and machine learning skills Proficiency in large-scale genomic data management and bioinformatic processing, including fluency with R/Bioconductor workflows and Python scientific stacks. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, and ideally with probabilistic programming tools such as Pyro or Stan. Experience with multimodal data fusion and building efficient, scalable data pipelines for large genomic datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference, and survival analysis for clinical outcome data. Scholarly record and collaboration A track record of first-author peer-reviewed publications (or preprints) appropriate to career stage, and the communication skills to work effectively across a team that spans multiple disciplines. City of Hope employees pay is based on the following criteria: work experience, qualifications, and work location. City of Hope is an equal opportunity employer. To learn more about our Comprehensive Benefits, please CLICK HERE . #PD
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First Seen At2026-05-31 18:23:37Z
Last Seen At2026-06-06 13:45:28Z
Last Checked At2026-06-06 13:45:28Z
Last Changed At2026-06-05 03:57:05Z
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