Senior Research Scientist (PREP0004541)

PREP Research Associate This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience (PREP) program. NIST supports research staff in collaborating with academic institutions on scientific projects of mutual interest. Employees in this role will perform technical work that underpins the scientific research of the collaboration. Research Title Machine Learning for Neutron Reflectometry Neutron reflectometry (NR) is a leading technique for studying disease‑relevant proteins or peptides at cell membranes under pharmaceutically relevant conditions. The Reflectometry‑driven Optimization And Discovery of Membrane Active Peptides (ROADMAP) project at NIST aims to create an autonomous biomolecule design infrastructure. The successful candidate will design and implement the AI component of ROADMAP, determining the sequence of NR measurements to build a comprehensive dataset within experimental time and resource constraints. Key Responsibilities Assess state‑of‑the‑art AI models for computer‑aided drug design, drug screening, and biochemical property prediction. Define data structures for the representation of antimicrobial peptides (AMPs) and their targets, focusing on natural language processing (NLP)‑based approaches and graph convolutional neural networks. Design and implement AI models to predict peptide antimicrobial properties and evaluate their prediction accuracy. Design and implement AI models to generate novel peptides with optimal properties. Study peptide representations associated with the AI models, defining metrics to characterize prediction performance. Provide guidance for the next batch of NR measurements, enabling iterative data acquisition and model refinement. Define AI‑specific metrics to assess the diversity of generated peptides and validate efficient exploration of the antimicrobial peptide space in collaboration with stakeholders. Write manuscripts to disseminate research findings. Qualifications A Ph.D. in computer science, computational biology, statistics, mathematics, or a related field. 5+ years of experience in machine learning applied to biochemistry. 5+ years of experience with state‑of‑the‑art generative and predictive AI tools in drug design and bioinformatics, with preferred experience in antimicrobial peptides. 5+ years of experience in natural language processing. 5+ years of experience with PyTorch and TensorFlow platforms for ML modeling. 5+ years of experience with popular AI modeling platforms, such as PyTorch and TensorFlow. 5+ years of experience with the PyTorch Geometric platform for building and evaluating Graph Neural Networks. Working knowledge of platforms for processing and analyzing molecular sequences, such as RDKit and pysmiles. 5+ years of experience developing prototypes of tools for data analysis in the bioinformatics domain. Strong oral and written communication skills. US citizenship preferred. Salary Range The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Actual compensation may vary based on location, skills, experience, and other factors. Total Rewards Johns Hopkins offers a total rewards package that supports employees' health, life, career, and retirement. Equal Opportunity Employer The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. The university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved. #J-18808-Ljbffr Johns Hopkins University