Predoctoral Appointee – Machine Learning

Job Description

Our mission is to accelerate scientific discovery by advancing trustworthy, privacy-preserving AI methodologies that can safely leverage sensitive scientific data. The Advanced Data Technologies and Federated Learning group at Argonne National Laboratory is seeking a highly motivated pre-doctoral appointee to develop and evaluate next-generation Privacy-Preserving Federated Learning (PPFL) techniques for large-scale biomedical applications. This position offers the opportunity to work at the intersection of machine learning, privacy technologies, and high-performance computing (HPC) while collaborating with leading scientists, clinicians, and data engineers.

The successful candidate will help shape new algorithms for learning from multimodal data, e. g. including imaging, clinical text, genomics, and digital health streams—and investigate the impact of differential privacy and privacy budgets on model fidelity and fairness. The work will take place in a multidisciplinary, innovation-oriented environment and will provide opportunities to publish research and present at top scientific venues.

Core responsibilities: Develop and evaluate PPFL algorithms that enable collaborative model training on distributed biomedical datasets without centralized data movement, leveraging frameworks such as APPFL/APPFLx and secure HPC environments. Create novel approaches for multimodal learning in federated settings, integrating structured EHR data, imaging, sensor data, and genomic information to improve model robustness, generalization, and interpretability.

Study privacy budgets and differential privacy mechanisms to quantify, optimize, and communicate tradeoffs between privacy guarantees and downstream model performance, with emphasis on biomedical use cases. Design and run large-scale experiments on DOE leadership-class computing systems, benchmarking federated optimization strategies, privacy mechanisms, and algorithmic scalability. Contribute to scientific discovery in computational biology and precision medicine, including applications such as disease risk prediction, phenotype modeling, digital pathology, and clinical decision support.

Disseminate research findings through peer-reviewed publications, technical reports, conference presentations, and open-source software contributions. Collaborate closely across Argonne’s multidisciplinary teams, including experts in HPC systems, AI frameworks, privacy-enhancing technologies, and biomedical data science, in alignment with Argonne’s strategic mission in computation and health-related AI. Position Requirements Required qualifications: Recently completed Master’s degree in computer science, biomedical engineering, applied mathematics, statistics, computational biology, or a related quantitative field.

Demonstrated experience in programming, with proficiency in Python and familiarity with numerical or scientific computing libraries (e. g.

, NumPy, PyTorch, TensorFlow). Strong aptitude for developing and evaluating machine learning models, including hands-on experience implementing algorithms for classification, regression, or representation learning. Ability to analyze complex datasets, design computational experiments, and interpret model performance in a scientifically rigorous manner.

Excellent written and verbal communication skills, with the ability to work effectively in interdisciplinary research teams. Ability to model Argonne’s core values of impact, safety, respect, integrity and teamwork. Desired qualifications: Experience with privacy-preserving machine learning concepts such as federated learning, differential privacy, or secure multiparty computation.

Familiarity with handling biomedical or multimodal data (e. g. , images, EHR, genomics, sensor data).

Exposure to large-scale computing environments or distributed systems concepts. Understanding of deep learning architectures (transformers, CNNs, RNNs) and approaches for multimodal fusion. Experience conducting research projects, contributing to publications, or presenting findings in academic or technical settings.

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