Ph.D. Candidate

**Job Description**
We are seeking a motivated Ph.D. candidate to join an interdisciplinary research program focused on developing probabilistic deep-learning models. This project aims to automatically extract biological and statistical knowledge from `in vivo` perturbational omics data, particularly from single-cell CRISPR technologies. The goal is to unravel `in vivo` pathways of immune cells, move towards precision medicine, and build predictive models for understanding developmental and disease states. The candidate will be embedded in both experimental and computational teams, benefiting from expertise in deep-learning model development and opportunities for direct experimental validation.

**Skills & Abilities**
� Experience in either machine learning or computational biology, with interest in both.
� Programming experience in Python.
� Excellent communication skills.
� Fluency in English.
� Collaborative personality with attention for detail.
� *Bonus:* Experience with training and validating Pytorch and/or JAX deep learning models.
� *Bonus:* Experience in single-cell or spatial omics data analysis.

**Qualifications**
Required Degree(s) in:
� Software engineering
� Computer science
� Data science
� Bioengineering
� Bioinformatics
� Engineering
� Physics or related (Master’s degree)

**Experience**
Other:
� Master’s degree in a relevant field.
� Experience in either machine learning or computational biology.
� Programming experience in Python.

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