Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants .
This position is funded for 3 year(s); continuation past 3 year(s) will be based on university need, performance, and/or availability of funding.
POSITION SPECIFICS
The U.S. National Science Foundation National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) and the Institute for Computational and Data Sciences (ICDS) at Penn State seeks an outstanding scientist to fill a Machine Learning Staff Scientist (Research Data Scientist - Intermediate Professional or Advanced Professional level) position dedicated to advancing the collaborative research of the Center’s Working Groups .
NCEMS is an interdisciplinary research Center positioned at the interface of data science with molecular and cellular biology. The Center provides leadership in the integration of diverse, publicly available datasets, enabling cross-disciplinary teams of scientists to synthesize knowledge and pursue fundamental questions at the forefront of the life sciences.
About the Position: Machine Learning Staff Scientists play a supporting role in enabling the research efforts of multidisciplinary scientific teams supported by NCEMS, typically contributing to 2-3 projects simultaneously.
Work Arrangement: This position has the potential to be a hybrid of remote and on-site work, with a minimum requirement of 3 days per week on-site at the Penn State University Park campus. This position does not permit fully remote work.
Collaborate with NCEMS Working Groups to design, develop, and evaluate machine learning approaches for integrating, analyzing, and visualizing molecular and cellular biology data across the central dogma and regulatory processes.
Prepare ML-ready datasets by leading data wrangling, harmonization, standardization, quality control, and documentation to support robust training and reuse across biological modalities.
Develop end-to-end ML workflows (feature/representation learning, training, validation, benchmarking, and uncertainty quantification) for multi-omics and related data types.
Build and optimize predictive and generative models (e.g., deep learning, probabilistic models, foundation-model adaptation, graph/neural sequence models) to support synthesis research questions.
Implement scalable training and inference pipelines using modern ML tooling (e.g., PyTorch /TensorFlow/JAX), version control, containers, and HPC/GPU resources.
Support the publication of intermediate data products, models, code, and documentation.
Stay up-to-date with the latest advancements in machine learning, AI for biology, and the rapidly evolving landscape of public molecular and cellular datasets.
Education and Experience:
M.S. or PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, Statistics, Data Science, or a related field is preferred .
Strong proficiency in Python for scientific computing and machine learning, including experience with common ML libraries/frameworks (e.g., PyTorch , TensorFlow, JAX, scikit-learn).
Demonstrated experience and understanding of core machine learning, deep learning and statistical methods such as: regression and generalized linear models; classification and clustering; dimensionality reduction; sequence and time-series modeling; deep learning architectures including CNNs, RNNs, GNNs, and transformers; generative modeling (e.g., diffusion and variational/auto-regressive approaches), representation learning and self-/weakly-supervised learning, natural language processing, computer vision, and causal inference.
Experience working with high-dimensional, large-scale molecular and cellular datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic/lipidomic, imaging-derived, single-cell, or multi-omics data), including appropriate preprocessing and normalization strategies for ML.
Solid understanding of molecular and cellular biology concepts sufficient to frame ML problems across the central dogma (sequence, expression, regulation, and protein function/structure) and to collaborate effectively with domain scientists.
Experience with software engineering practices for research-grade code, version control (Git), reproducible environments (containers/ conda ), HPC/GPU computing.
Publications in peer-reviewed journals demonstrating contributions to the field.
Experience supporting/contributing to multi-PI projects.
Candidates must also demonstrate a commitment to ethical conduct and research integrity, strong work ethic, strong interpersonal and written communication skills, and the ability to work well in a team environment.
Applicaiton materials: Required documents include the following:
Benefits:
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being. For more detailed information, please visit our Benefits Page.
MINIMUM EDUCATION, WORK EXPERIENCE & REQUIRED CERTIFICATIONS
If filled as Research Data Scientist - Intermediate Professional, this position requires:
Bachelor's Degree
1+ years of relevant experience; or an equivalent combination of education and experience accepted
Required Certifications:
None

If filled as Research Data Scientist - Advanced Professional, this position requires:
Bachelor's Degree
3+ years of relevant experience; or an equivalent combination of education and experience accepted
Required Certifications:
None
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
Penn State does not sponsor or take over sponsorship of a staff employment Visa. Applicants must be authorized to work in the U.S.
SALARY & BENEFITS
The salary range for this position, including all possible grades, is $61,800.00 - $115,100.00.
Salary Structure - Information on Penn State's salary structure
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans and substantial paid time off which includes holidays, vacation and sick time. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children. For more detailed information, please visit our Benefits Page .
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here .
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
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