Open Positions

Our lab offers a collaborative and innovative research environment where you can work on cutting-edge problems at the intersection of computational biology and machine learning. We value diversity, creativity, and scientific rigor.

Why Join Our Lab?

  • 🚀 Cutting-edge Research: Work on projects at the forefront of computational biology and AI
  • 🤝 Collaborative Environment: Join a diverse team of researchers from multiple disciplines
  • 📚 Professional Development: Access to conferences, workshops, and training opportunities
  • 💻 State-of-the-art Resources: Access to high-performance computing clusters and GPUs
  • 🌍 International Collaboration: Work with leading researchers worldwide
  • 📈 Career Growth: Strong track record of alumni success in academia and industry
Postdoctoral Positions

Postdoctoral Researcher in Machine Learning for Drug Discovery

We are seeking a highly motivated postdoctoral researcher to develop novel machine learning approaches for drug discovery and molecular design.

Requirements:

  • PhD in Computer Science, Computational Biology, or related field
  • Strong background in machine learning and deep learning
  • Experience with molecular modeling or cheminformatics (preferred)
  • Excellent programming skills in Python
  • Strong publication record

Application Materials:

  • CV including publication list
  • Research statement (2-3 pages)
  • Three reference letters
  • Representative publications (2-3 papers)

Application Deadline: Rolling basis

PhD Student Positions

PhD Positions in Computational Biology

We have multiple PhD positions available for students interested in applying computational methods to biological problems. Projects include:

  • Protein Structure Prediction: Developing deep learning models for protein folding
  • Single-Cell Analysis: Machine learning for understanding cellular heterogeneity
  • Drug-Target Interaction: Predicting molecular interactions using graph neural networks
  • Genomic Medicine: Identifying disease mechanisms from multi-omics data

Requirements:

  • Bachelor’s or Master’s degree in Computer Science, Biology, Mathematics, or related field
  • Strong programming skills (Python, R, or similar)
  • Interest in interdisciplinary research
  • Excellent communication skills

How to Apply: Applications should be submitted through the university’s graduate admissions portal. Please mention Prof. Jane Smith as your preferred advisor.

Application Deadline: December 15 for Fall admission

Undergraduate Research Opportunities

Summer Research Program

Our lab participates in the Summer Undergraduate Research Fellowship (SURF) program. Selected students will:

  • Work on independent research projects with mentorship from lab members
  • Attend weekly lab meetings and journal clubs
  • Present research findings at the end-of-summer symposium
  • Receive a competitive stipend

Eligibility:

  • Completed at least 2 years of undergraduate study
  • GPA of 3.5 or higher
  • Background in computer science, biology, or related field

Application Process:

  • Online application form
  • Transcript
  • One letter of recommendation
  • Short essay on research interests (500 words)

Application Deadline: February 1 for Summer positions

How to Apply

Interested candidates should send their application materials to Prof. Jane Smith at jane.smith@example.edu.

Please use “Application: [Position Type]” in the subject line. We review applications on a rolling basis and will contact qualified candidates for interviews.

Our lab is committed to diversity and inclusion. We encourage applications from underrepresented groups in STEM.

Contact Prof. Smith
Visiting Researchers

We welcome visiting researchers and scholars who wish to collaborate with our lab. Visiting positions are available for:

  • Sabbatical Visitors: Faculty members on sabbatical leave
  • Visiting PhD Students: Students from other institutions (3-12 months)
  • Industry Collaborators: Researchers from companies interested in collaboration

Please contact Prof. Smith directly to discuss visiting opportunities.