Forskningsassistent inom beräkningsbaserad protein- och peptiddesign
Core
Develop, benchmark, and run deep learning pipelines for structure prediction, conformational sampling, and de novo peptide/protein design.
Role type
Senior IC computational biologist (machine learning)
Builds
Production ML models for complex membrane proteins, GPCRs, and cyclic peptides
Domain
Academic research in molecular cell biology and computational biology
Deliverable
production ML models
Required skills
Python, deep learning frameworks (PyTorch, JAX), structural biology principles, structure prediction tools (AlphaFold, ESMFold, PyMOL)
Preferred skills
HPC job execution (Linux/SLURM), molecular dynamics, GPCR structural biology, cyclic peptide modeling, software development practices (Git, Docker/Singularity)
Technologies
PyTorch, JAX, AlphaFold, ESMFold, PyMOL, Git, Docker, Singularity, SLURM
Responsibilities
Develop and benchmark deep learning pipelines for structure prediction and de novo design; Perform structure modeling for complex membrane proteins, GPCRs, and cyclic peptides; Manage computational workflows and job runs in HPC clusters; Analyze biophysical data and maintain clean code repositories
Seniority
Mid-level, hands-on IC