$47K - $64K/yr
... cutting-edge machine learning to transform omics data into actionable therapeutic insights ... D. in bioinformatics, computational biology, computer science, cancer biology, molecular biology ...
$47K - $64K/yr
... cutting-edge machine learning to transform omics data into actionable therapeutic insights ... D. in bioinformatics, computational biology, computer science, cancer biology, molecular biology ...
... bioinformatics, and related disciplines. Its faculty lead externally funded research and ... Develop and apply statistical, computational, machine-learning, and deep-learning approaches to ...
... bioinformatics, and related disciplines. Its faculty lead externally funded research and ... Develop and apply statistical, computational, machine-learning, and deep-learning approaches to ...
Cardiovascular Research, Computational Models, Data Visualization, Machine Learning (ML), Neuroscience, Pharmacology, Python (Programming Language), Respiratory Pharmacology, Risk Assessments, Safety ...
Cardiovascular Research, Computational Models, Data Visualization, Machine Learning (ML), Neuroscience, Pharmacology, Python (Programming Language), Respiratory Pharmacology, Risk Assessments, Safety ...
... Bioinformatics, or related quantitative fields, and 2-3 years of experience or an equivalent ... Familiarity with machine learning, AI, signal processing, or quantitative modeling approaches.
... Bioinformatics, or related quantitative fields, and 2-3 years of experience or an equivalent ... Familiarity with machine learning, AI, signal processing, or quantitative modeling approaches.
... Bioinformatics, at Penn State College of Medicine in Hershey, PA invites applications for a ... Prior publications or preprints in biostatistics, statistics, clinical trials, machine learning ...
... Bioinformatics, at Penn State College of Medicine in Hershey, PA invites applications for a ... Prior publications or preprints in biostatistics, statistics, clinical trials, machine learning ...
... bioinformatics or machine learning) with a history of impactful scientific publications and/or presentations. * Experienced in genetics and conducting analyses that integrate genetics, genomics and ...
... bioinformatics or machine learning) with a history of impactful scientific publications and/or presentations. * Experienced in genetics and conducting analyses that integrate genetics, genomics and ...
$67.4K is the 25th percentile. Wages below this are outliers.
$59.6K - $67.8K
27% of jobs
$67.8K - $76K
15% of jobs
$76K - $84.2K
6% of jobs
The median wage is $85.4K / yr.
$84.2K - $92.4K
15% of jobs
$92.4K - $100.7K
9% of jobs
$100.7K - $108.9K
2% of jobs
$115K is the 75th percentile. Wages above this are outliers.
$108.9K - $117.1K
2% of jobs
$117.1K - $125.3K
2% of jobs
$125.3K - $133.5K
10% of jobs
$133.5K - $141.7K
11% of jobs
$141.7K - $149.9K
2% of jobs
$59.6K
$94.7K
$149.9K
A Bioinformatics Machine Learning job involves applying machine learning techniques to analyze and interpret biological data, such as genomics, proteomics, and medical records. Professionals in this field develop algorithms, build predictive models, and enhance data-driven research in areas like personalized medicine and drug discovery. They work with large datasets, applying deep learning, neural networks, and other AI methods to extract meaningful insights. The role requires expertise in biology, statistics, and programming languages like Python or R.
In a Bioinformatics Machine Learning role, your daily tasks usually involve developing and tuning machine learning models to analyze large biological datasets, such as genomics or proteomics data. You'll collaborate closely with researchers, biologists, and data scientists to understand project goals, interpret results, and refine analytical approaches. Routine work includes coding, troubleshooting algorithms, visualizing data outputs, and documenting findings for internal teams or publication. The role often requires balancing independent analysis with teamwork and regular communication across disciplines, making it both technically challenging and highly collaborative.
A successful Bioinformatics Machine Learning professional needs a solid background in biology, statistics, and computer science, often backed by an advanced degree such as a Master's or PhD in bioinformatics, data science, or a related field. Proficiency with programming languages like Python or R, experience with machine learning libraries (e.g., TensorFlow, scikit-learn), and knowledge of version control systems are typical requirements, and relevant certifications can be beneficial. Strong problem-solving abilities, effective communication skills, and the capacity to work collaboratively in interdisciplinary teams set candidates apart. These skills are crucial for designing robust computational models, interpreting complex biological data, and translating findings into actionable insights in research or clinical settings.
$47K - $64K/yr
Full-time
Re-posted 12 days ago
A postdoctoral position is available in the Wang Laboratory at UPMC Hillman Cancer Center. We seek highly motivated scientists with expertise in computational genomics/AI and/or translational cancer biology to join our dynamic, well-funded research program at the frontier of cancer genetics and precision oncology.
Candidates with training in both computational genomics and cancer biology are especially welcome to apply-our lab thrives on bridging computational discovery with experimental validation, and dual-skilled scientists will find exceptional opportunities to lead integrative projects spanning both domains.
Our lab operates at the intersection of computational innovation and experimental cancer biology. Funded by over $11.5 million in research grants-including four active DOD Breakthrough Awards totaling $5.1 million-we offer an exceptional environment for ambitious postdoctoral scientists to make high-impact discoveries with direct clinical translational potential.
Why Join Us?
"Dark matter" cancer genetics: Pioneer discoveries in uncharted areas of breast cancer genetics, including recurrent gene fusions (ESR1-CCDC170, BCL2L14-ETV6, RAD51AP1-DYRK4) and intragenic rearrangements (IGRs)-a largely unexplored class of genetic aberrations.
Precision immuno-oncology: Develop next-generation biomarkers (IGR burden, TAA burden, IMPREG signature) for immunotherapy patient selection, especially for TMB-low and PD-L1-negative cancers where current tools fall short.
AI-powered precision oncology: Build mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights.
Translational impact: Work alongside oncologists on a rapid discovery-to-clinic pipeline, with prospective clinical study and clinical trial design directly linked to laboratory findings.
Proven trainee success: Our postdoctoral alumni have received prestigious fellowships from the DOD, Susan G. Komen Foundation, Hillman Cancer Center, and the Gottfried Family Women's Health Award, totaling over $1.3M in trainee funding.
High-impact publications: Join a track record of publications in Nature Biotechnology, Nature Communications, Science, PNAS, Cancer Discovery, Cancer Research, Cancer Immunology Research, and Clinical Cancer Research.
Research Area 1: Computational Genomics & AI-Driven Precision Oncology
This position focuses on developing and applying advanced computational and AI methods to tackle major challenges in cancer genomics and precision medicine. Specific areas include:
1) Building the Genomics to Knowledge (G2K) agentic AI framework for automated transformation of multi-omics data into biological insights through iterative, hypothesis-driven computational analysis. 2) Characterizing the landscape of structural mutations-including intragenic rearrangements (IGRs)-across cancer types and modeling their impact on the tumor immune microenvironment and immunotherapy response. 3) Developing clinical-grade mechanism-driven AI models (iGenSig-AI) for predicting responses to targeted therapies and immunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden, IMPREG signature) for precision immuno-oncology panels.
Research Area 2: Translational Cancer Biology & Immunobiology
This position focuses on the experimental validation and biological characterization of newly discovered genetic targets at the interface of cancer genetics, pathobiology, and immunobiology. Specific areas include:
1) Investigating the functional roles of recurrent gene fusions (ESR1-CCDC170, BCL2L14-ETV6, RAD51AP1-DYRK4) and novel intragenic rearrangements in breast and ovarian cancer progression, immune evasion, and therapy resistance. 2) Characterizing novel structural mutations in actionable kinases and evaluating genotype-directed therapeutic strategies in preclinical models. 3) Performing in vitro and in vivo validation of computationally predicted cancer targets, including studies of epithelial-mesenchymal transition, drug resistance, and immune dysfunction. 4) Exploring immunotherapeutic strategies guided by biomarker status, including combination therapies with -catenin inhibitors and immune checkpoint blockade in triple-negative breast cancer.
Qualifications:
Ph.D. in bioinformatics, computational biology, computer science, cancer biology, molecular biology, immunology, genetics, or a related field. Depending on research focus, relevant experience may include machine learning, multi-omics data analysis, cancer genomics, immunogenomics, or systems biology of transcriptional regulation (strong programming skills in Python, R, or equivalent expected), and/or cell and molecular biology techniques, animal models, immunology assays, or translational research. Candidates with combined computational and experimental skills are especially encouraged to apply.
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Colleges, universities, and professional schools
10,000+ Employees
Pittsburgh, PA, US
1893
