PREP Research Associate
This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research Title
Machine Learning for Neutron Reflectometry
The work will entail:
Neutron reflectometry (NR) is one of the techniques of choice to fill the data gap for disease-relevant proteins or peptides at cell membranes under pharmaceutically relevant conditions. A NR-based innovative measurement approach, called the Reflectometry-driven Optimization And Discovery of Membrane Active Peptides (ROADMAP), is currently being developed at NIST to create an autonomous biomolecule design infrastructure. Antimicrobial peptides (AMP), which can efficiently disrupt bacteria membrane upon interaction with the lipid bilayer, are the focus of ROADMAP.
The successful candidate will design and implement the AI component of ROADMAP, which will determine the sequence of NR measurements leading to a comprehensive dataset within the experimental time and resource constraints. The main tasks of the recipient will be: to develop data structures that capture AMP properties, multimodal NR observables and experimental conditions; to select appropriate AI architectures; to validate the resulting AI framework on AMPs of interest in the context of ROADMAP.
Key responsibilities will include but are not limited to:
§ 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 AMPs and their targets. Specific focus should be on natural language processing (NLP)-based approaches and graph convolutional neural networks.
§ Design and implement AI models for the prediction of peptide antimicrobial properties and assess their prediction accuracy.
§ Design and implement AI models for the generation of novel peptides with optimal properties.
§ Study the peptide representation associated with the implemented AI models, define metrics to characterize the prediction performance.
§ Provide guidance for the next batch of NR measurements, which will yield additional training data in an iterative fashion.
§ Define AI-specific metrics to assess the diversity of the AI-generated peptides, validate the efficient exploration of the antimicrobial peptide space in collaboration with the stakeholders.
§ Write manuscripts to disseminate the work.
§ A Ph.D. in computer science, computational biology, statistics, mathematics, or a related field.
§ 5+ years of experience in machine learning with application to biochemistry.
§ 5+ years of experience with state-of-the-art generative and predictive AI tools in the context of drug design and bioinformatics, with strongly preferred specific experience in the space of antimicrobial peptides.
§ 5+ years of experience in natural language processing.
§ 5+ experience with Pytorch and Tensorflow platforms for ML modeling.
§ 5+ years of experience with popular platforms for AI modeling, such as PyTorch and TensorFlow.
§ 5+ years of experience with the PyTorch Geometric platform for building and evaluating Graph Neural Networks.
§ Working knowledge of popular platforms for processing and analyzing molecular sequences, such as RDkit and pysmiles.
§ 5+ years of experience in 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. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.
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After receiving a conditional offer, the successful candidate(s) for this position will be subject to a pre-employment background check including education verification. When deciding whether a candidate’s conviction history is job-disqualifying, the University considers the nature and gravity of the offense, the time that has passed since the conviction, and the nature of the job being sought.
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