Looking for a job

TITLI SARKAR

@titlisarkar

Computational Scientist at Frederick National Laboratory for Cancer Research designing in-silico peptide and small-molecule binders for drug targets.

United States

Message

What I'm looking for

I'm seeking opportunities relevant to AI-enabled healthcare, computational drug discovery, immuno-inflammatory research, biomedical data analysis, and translational R&D.

At Frederick National Laboratory for Cancer Research, I design in-silico peptide and small-molecule binders for drug targets, using structure-guided and ligand-based approaches. I also analyze high-resolution microscopy images to characterize tissue- and cell-level spatial patterns.

At FNLCR and the ATOM Consortium, I developed, validated, and tested QSAR machine-learning models for drug-target compounds and supported AI/ML workflows for ADME prediction and generative molecular design. My research at the University of Louisiana at Lafayette included developing computational methods and a Python/Django tool for protein-structure analysis, similarity search, and interaction studies.

Experience

Work history, roles, and key accomplishments

Current

Computational Scientist

Current

Frederick National Laboratory for Cancer Research

Apr 2023 - Present (3 years 6 months)

Designs in-silico peptide and small-molecule binders for drug targets, performs protein modelling, docking, molecular dynamics, and develops computational models on HPC. Analyzes high-resolution microscopy images and collaborates with domain experts to translate research questions into computational models.

Data Scientist

Frederick National Laboratory for Cancer Research

Feb 2022 - Apr 2023 (1 year 2 months)

Developed and validated QSAR machine-learning models on HPC/cloud platforms for cancer drug-discovery projects. Built AI/ML models for ADME drug-property prediction and supported ligand-based computational drug-discovery workflows.

Python PyTorch TensorFlow Keras scikit learn Pandas NumPy QSAR ADME HPC AWS Docker Git Machine Learning Deep Learning Molecular Design

Research Assistant

University of Louisiana at Lafayette

Aug 2016 - Dec 2021 (5 years 4 months)

Conducted interdisciplinary clinical research involving machine learning, data mining, and data analysis in biochemistry. Developed mathematical and computational methods for protein-structure similarity, clustering, and drug-binding-site identification using HPC and parallel processing.

Python Django PostgreSQL HPC Parallel Processing Machine Learning Data Mining Protein Structure Analysis TSR CUDA Python

Data Science Summer Intern

Bayer Crop Science

May 2020 - Aug 2020 (3 months)

Applied computer-aided drug design using protein-similarity search to select novel compounds. Used deep-learning, similarity-comparison, and statistical approaches in applied biological research.

Lecturer

Ashutosh College & Scottish Church College

May 2015 - Aug 2016 (1 year 3 months)

Principal instructor for Parallel Processing, teaching CUDA C, pyCUDA, Database Management Systems, Distributed Architecture, and Operating Systems at graduate level.

CUDA CUDA Python Database Management Systems Distributed Architecture Operating System Teaching

Software Developer Intern

Egnaroinc

Sep 2014 - Jun 2015 (9 months)

Worked on backend development using REST API and Java, automated testing with JUnit, MVC architecture using Play Framework, and NoSQL database development using MongoDB. Set up Play and Mongo clusters over LAN and supported production operations.

Java REST APIs Junit Play Framework MongoDB NoSQL MVC Hardware Cluster Setup