Intern, Genomic Sciences & Target Research
About The Position
The Summer Intern will support the Genomic Sciences and Target Discovery team in strengthening the rigor, efficiency, and scalability of early target assessments through the application of AI/ML methods. This role will focus on building computational frameworks, data and AI-driven workflows that enable real-time competitive intelligence, improved target dossier generation, and systematic target prioritization. The intern will gain hands-on experience at the interface of data science, machine learning, and early discovery research, with opportunities to contribute directly to platform development that informs neuroscience and psychiatric drug discovery.
Requirements
- Current enrollment in a Master’s or Doctoral program in one of the following fields: Computer Science Data Science or Machine Learning Computational Biology Bioinformatics Computational Neuroscience Or a related quantitative discipline
Nice To Haves
- Foundational programming skills, including experience with Python and relevant python libraries (e.g., pandas, numpy, scikit‑learn, beautifulsoup, selenium, or similar).
- Basic understanding of machine learning (supervised/unsupervised learning, model evaluation, feature engineering, etc.), AI and large language model concepts.
- Familiarity with data handling, including cleaning, parsing, and working with structured/unstructured datasets.
- Strong analytical and problem‑solving skills and ability to work independently.
- Clear communication skills, especially in summarizing findings and documenting workflows.
- Experience with web‑scraping tools and frameworks such as BeautifulSoup, Scrapy, or Selenium.
- Familiarity with LLMs, agentic frameworks, or autonomous AI tools for information extraction, summarization, or workflow automation (vLLM, Ollama, OpenAI and LangChain APIs).
- Exposure to bioinformatics or genomics databases (e.g. OpenTargets).
- Understanding drug discovery concepts, especially target identification, validation, and prioritization.
- Experience developing data dashboards or lightweight visualization tools using libraries such as Plotly, Dash, Streamlit, or Tableau.
- Hands‑on coursework or projects involving machine learning model development, evaluation, and optimization.
- Working knowledge of containerization (e.g. Docker).
- Ability to integrate and interpret multi‑modal data sources, including literature, omics data, and competitive intelligence inputs.
- Strong organizational skills with the ability to document code, analyses, and workflows clearly.
- Intellectual curiosity and a demonstrated interest in neuroscience, psychiatric disorders, or computational approaches to biology.
- Having prior wet lab experience is highly desirable.
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What This Job Offers
Career Level
Intern
