10405 - Research Associate
UOE07 - £39,347 - £46,974
School of Informatics
Contract Type - Fixed term - 24 Months
Full Time - 35 Hours Per Week
The Opportunity:
We are looking for a postdoctoral researcher to join the Biomolecular Control Group at the University of Edinburgh. The group develops computational methods for the design of molecular systems for biotechnology and future therapy.
The post is part of the CYBER Mission Award in Engineering Biology between the University of Bristol, University of Newcastle and University of Edinburgh. Our consortium aims to engineer cells designed to tackle environmental challenges and support their future deployment into real-world ecosystems.
We want to find ways to make a real positive impact on the environment by combing our growing capabilities in engineering biology and AI with our understanding of ecology and natural environments. To this end, we will employ a cutting-edge combination of molecular and ecological datasets in collaboration with an array of industry partners.
The post-holder will build predictive machine learning models to guide the cell engineering work of the consortium. We are looking for candidates with experience in applied machine learning, ideally to biological or molecular data, including, but not limited to, classic/deep learning, active learning, graph neural networks, or self-supervised learning.
While prior experience with biological data is not essential, we will be looking for candidates who are willing to engage with the biological design task and collaborate closely with the postdocs in our wetlab partner labs. The post-holder will be expected to actively engage with the other four postdocs recruited for the project and we have a unique multi-institution rotation scheme to facilitate these interactions.
Applicants with a biological background and proven hands-on experience with machine learning methods are particularly encouraged to apply. More details can be found in the CYBER project website.
About the lab: We are a diverse, international, and multidisciplinary team working at the cutting-edge of machine learning applied to biological questions. Some of our recent breakthroughs include the discovery of new therapeutic molecules, sequence-to-expression prediction with small data, active learning for production of complex biomolecules, and integration of machine learning and genome-scale mechanistic models.
As a team, we gather expertise in many computational and mathematical methods, including nonlinear dynamics, machine learning, optimization, network theory and stochastic processes. Interested candidates are encouraged to check our lab website to find areas of synergy or where you can bring complementary expertise to the lab.
The Biomolecular Control Group is co-located at the School of Informatics and School of Biological Sciences. The School of Informatics is widely recognized as one of the birthplaces of AI and is home to some of the latest developments in the field.
As a member of our lab, you will join the Edinburgh Centre for Engineering Biology and its thriving community of researchers working on biological solutions to global challenges.
Our team ethos is based on mutual learning, strong peer-to-peer support, and a drive to support the career growth of our members. We offer multiple opportunities for networking and skills development, for example through guidance and co-creation of student research projects, or engaging with multi-stakeholder initiatives such as the Science for Sustainability Hub.
Edinburgh is an exciting European capital, with excellent quality of life and a thriving science, arts, and cultural scene, as well as access to beautiful natural landscapes.
The post is for 2 years; there is potential of extending the post for 3 additional years through a separate funding stream. We expect candidates to have a promising publication record, excellent mathematical and computational skills, a demonstrated willingness to learn new concepts from other disciplines, and an excellent ability to communicate their work effectively.
Your skills and attributes for success:
Essential:
Ph.D. degree (or nearing completion) in a suitable field
Promising publication record
Experience in applied machine learning to real-world data
Excellent communication skills in multidisciplinary environments
Desirable:
Experience working with biological datasets
School of Informatics
Contract Type - Fixed term - 24 Months
Full Time - 35 Hours Per Week
The Opportunity:
We are looking for a postdoctoral researcher to join the Biomolecular Control Group at the University of Edinburgh. The group develops computational methods for the design of molecular systems for biotechnology and future therapy.
The post is part of the CYBER Mission Award in Engineering Biology between the University of Bristol, University of Newcastle and University of Edinburgh. Our consortium aims to engineer cells designed to tackle environmental challenges and support their future deployment into real-world ecosystems.
We want to find ways to make a real positive impact on the environment by combing our growing capabilities in engineering biology and AI with our understanding of ecology and natural environments. To this end, we will employ a cutting-edge combination of molecular and ecological datasets in collaboration with an array of industry partners.
The post-holder will build predictive machine learning models to guide the cell engineering work of the consortium. We are looking for candidates with experience in applied machine learning, ideally to biological or molecular data, including, but not limited to, classic/deep learning, active learning, graph neural networks, or self-supervised learning.
While prior experience with biological data is not essential, we will be looking for candidates who are willing to engage with the biological design task and collaborate closely with the postdocs in our wetlab partner labs. The post-holder will be expected to actively engage with the other four postdocs recruited for the project and we have a unique multi-institution rotation scheme to facilitate these interactions.
Applicants with a biological background and proven hands-on experience with machine learning methods are particularly encouraged to apply. More details can be found in the CYBER project website.
About the lab: We are a diverse, international, and multidisciplinary team working at the cutting-edge of machine learning applied to biological questions. Some of our recent breakthroughs include the discovery of new therapeutic molecules, sequence-to-expression prediction with small data, active learning for production of complex biomolecules, and integration of machine learning and genome-scale mechanistic models.
As a team, we gather expertise in many computational and mathematical methods, including nonlinear dynamics, machine learning, optimization, network theory and stochastic processes. Interested candidates are encouraged to check our lab website to find areas of synergy or where you can bring complementary expertise to the lab.
The Biomolecular Control Group is co-located at the School of Informatics and School of Biological Sciences. The School of Informatics is widely recognized as one of the birthplaces of AI and is home to some of the latest developments in the field.
As a member of our lab, you will join the Edinburgh Centre for Engineering Biology and its thriving community of researchers working on biological solutions to global challenges.
Our team ethos is based on mutual learning, strong peer-to-peer support, and a drive to support the career growth of our members. We offer multiple opportunities for networking and skills development, for example through guidance and co-creation of student research projects, or engaging with multi-stakeholder initiatives such as the Science for Sustainability Hub.
Edinburgh is an exciting European capital, with excellent quality of life and a thriving science, arts, and cultural scene, as well as access to beautiful natural landscapes.
The post is for 2 years; there is potential of extending the post for 3 additional years through a separate funding stream. We expect candidates to have a promising publication record, excellent mathematical and computational skills, a demonstrated willingness to learn new concepts from other disciplines, and an excellent ability to communicate their work effectively.
Your skills and attributes for success:
Essential:
Ph.D. degree (or nearing completion) in a suitable field
Promising publication record
Experience in applied machine learning to real-world data
Excellent communication skills in multidisciplinary environments
Desirable:
Experience working with biological datasets
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