Multiple research avenues are pursued within our laboratory, offering the successful candidate the autonomy to develop and lead their own independent research projects aligned with the lab’s focus. Prospective projects encompass:
- Unravelling emergent dynamical laws from complex mathematical models of protein-protein interactions. Employing symbolic regression techniques on simulated data enables the extraction of simplified rate laws, integral for constructing models of multi-component signalling pathways. These models could then be used investigate signal transduction heterogeneity resulting from fluctuations in protein levels or to design synthetic multicomponent signalling cascades de novo.
- Crafting dynamic, multi-pathway signal transduction models by integrating prior knowledge networks (PKNs) with phosphoproteomic data. Transforming PKNs, which consolidate knowledge graphs, pathway databases, and protein structure information, into simulatable models allows for validation against dynamic multi-condition proteomics datasets. The primary objective is to discern the regulatory ‘grammar’ governing phosphorylation dynamics, elucidating molecular mechanisms or cellular processes that drive signalling dynamics.
- Deciphering cell cycle dynamics using pseudo-time embeddings derived from snapshot mass cytometry data. Leveraging low-dimensional embeddings to temporally order unsynchronized cell populations based on cell cycle marker expression facilitates the learning of dynamic models from static snapshot data. Leveraging mass cytometry datasets spanning diverse experimental conditions – including growth factor stimulation, genetic modifications, and small molecule drug interventions – aims to unveil the dependency of cell cycle dynamics on upstream signalling.
The post holder will be responsible for:
- Leading and developing specific research projects
- Developing and maintaining computational tools
- Applying methods to biological data and interpreting results
- Collaborating with national and international partners
- Presenting work at lab meetings as well as national and international meetings
- Publishing scientific results
- Assisting in training graduate students and other members of the laboratory
Essential
- PhD in a relevant field or in the final stages of PhD submission
- Good knowledge and experience in coding (Python/Julia/C or similar languages)
- Technical expertise in mathematical modelling and/or machine learning
- An interest in applying computational methods to biological problems
- A demonstrated ability to generate and pursue independent research ideas
- Excellent communication skills, written and verbal as evidenced by publications, preprints and/or conference presentations
- Cross-disciplinary mindset, collaborative attitude, and teamwork experience
- Dedication to reproducible research and open science
- A cover letter that is tailored to the lab and the position
Desirable
- Foundational knowledge in Cell Biology, Biochemistry and/or Systems Biology
- Foundational knowledge in Mathematics, Physics, Statistics and/or Data Science
- Background in numerical methods for optimization and solving differential equation models
- Familiarity with modern software development practices and high-performance computing
- Good command of a machine learning framework (jax/pytorch or similar)
Experience working in interdisciplinary environments
Online