Postdoctoral Fellow in Scientific Machine Learning – Fröhlich lab

at  Springer Nature

POSTDOCTORAL FELLOW IN SCIENTIFIC MACHINE LEARNING – FRÖHLICH LAB

Employer
Francis Crick Institute
Location
London (Central), London (Greater) (GB)
Salary
From £41,935 per annum plus benefits, subject to skills and experience
Closing date
29 Apr 2024
Discipline
Life Science
Job Type
Postdoctoral
Employment - Hours
Full time
Duration
Fixed term
Qualification
PhD
Sector
Academia

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KEY EXPERIENCE AND COMPETENCIES

The post holder should embody and demonstrate our core Crick values:
Bold; Open; Collegial, in addition to the following:

ABOUT US

At the Crick, we conduct research at the forefront of biomedical research. We combine rigour with an open and collaborative culture, and are outward-looking, reflecting our status as a partnership of six organisations aiming to pool knowledge, ideas and resources.
We have a wide research portfolio with no divisions or departments, bringing biomedical researchers together with clinicians, physical scientists and applied scientists from our pharmaceutical partners.
We aim to attract the most talented researchers and support them to tackle innovative research questions. Our science technology platforms provide our researchers with access to state-of-the-art technology and expertise.

We provide an excellent learning environment with dedicated education programmes in public engagement with science, education and personal development, and a postdoc training programme that prepares scientists for leadership roles in science.

• All offers of employment are subject to successful security screening and continuous eligibility to work in the United Kingdom.
• If you require a visa to work in the UK we will help support your application should you be successful

THE ROLE

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
• 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

We provide an excellent learning environment with dedicated education programmes in public engagement with science, education and personal development, and a postdoc training programme that prepares scientists for leadership roles in science.

• All offers of employment are subject to successful security screening and continuous eligibility to work in the United Kingdom.
• If you require a visa to work in the UK we will help support your application should you be successfu