inceptive
Computational design of biological experiments for model development
$135K–$240K
Berlin, Germany or Palo Alto, CA or Zurich, Switzerland +3 · Full-time · Posted Jul 16, 2026
- Location
- Berlin, Germany or Palo Alto, CA or Zurich, Switzerland
Berlin, Berlin, Germany
Palo Alto, California, United States
Zürich, Zürich, Switzerland - Experience
- 3+ years of post-PhD experience is preferred; no minimum required years stated.
- Education
- PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience.
About the job
Design and analyze computational and biological experiments to evaluate biological foundation models and guide their development, data strategy, and experimental priorities.
What you'll do
- Investigate how data and training choices affect model performance, and develop biologically meaningful evaluations and benchmarks.
- Identify and analyze biological datasets; build pipelines for preprocessing, quality control, and exploratory analysis.
- Design rigorous studies with biologists and AI researchers, interpret model behavior, and communicate findings across teams.
What they ask for
- PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience, with publications or open-source tooling.
- Experience analyzing high-throughput sequencing data, with robust statistical analysis.
- Experience collaborating with AI/machine-learning researchers or applying machine learning or generative AI to scientific problems.
- Python programming and familiarity with current machine-learning methods, biological datasets, and high-throughput assays.
Nice to have
- 3+ years of post-PhD experience in computational biology, biostatistics, machine-learning research, or a related field.
Pay
$135K – $240K USD base salary; bonus and equity amounts not stated
Tools
Python, RNA-seq, functional genomics / transcriptomics, MPRA, generative foundation models, representation learning
AI focus
Evaluate biological foundation models, investigate their behavior and limitations, and use findings to guide model development and therapeutic design objectives.
Model-written summary · the original posting is authoritative