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Temporary Machine Learning Postdoc Jobs in Pittsburgh, PA

Fujitsu

Senior Researcher, AI Security

Pittsburgh, PA · On-site

We are seeking a highly motivated and talented research scientist working in machine learning (ML ... postdoctoral research or impactful projects. * Proficiency in ML and AI programming written in ...

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Temporary Machine Learning Postdoc information

What skills and qualifications are needed to thrive as a temporary machine learning postdoc?

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a temporary machine learning postdoc expect to engage in?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is a temporary machine learning postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

What is the difference between Temporary Machine Learning Postdoc vs Data Scientist?

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

What are the most commonly searched types of Machine Learning Postdoc jobs in Pittsburgh, PA?

The most popular types of Machine Learning Postdoc jobs in Pittsburgh, PA are:

What are popular job titles related to Temporary Machine Learning Postdoc jobs in Pittsburgh, PA?

For Temporary Machine Learning Postdoc jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Postdoc jobs in Pittsburgh, PA look for?

The top searched job categories for Temporary Machine Learning Postdoc jobs in Pittsburgh, PA are:

Temporary Machine Learning Postdoc jobs near you

Post Doctoral.Post Doctoral.Associate

University of Pittsburgh

Pittsburgh, PA

$47K - $64K/yr

Full-time

Re-posted 19 days ago

Job description

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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