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Up Directory CCL 21.06.15 Investigator/Senior Investigator - Computational Molecular Physics - Roivant Sciences
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To: jobs at ccl.net
Date: Fri Jun 25 13:12:55 2021
Subject: 21.06.15 Investigator/Senior Investigator - Computational Molecular Physics - Roivant Sciences
At Roivant, we are passionate about discovering and developing new drugs
to impact patients lives. Since its inception in 2014, Roivant has launched over 20 
portfolio companies (Vants), overseen 5 successful IPOs, established a $3B partnership 
with a global pharma, built a pipeline of over 40 assets across various modalities and 
therapeutic areas, and delivered 8 successful phase 3 readouts.

Roivant is currently building new capabilities in drug discovery and expanding its 
existing development engine to become the worlds leading tech-enabled pharmaceutical 
company. Roivants drug discovery capabilities are driven by our computational discovery 
platform, which combines preeminent physics-based tools with deep expertise in machine 
learning to generate unprecedented predictive power that can tackle previously intractable 
discovery challenges. The tight integration of this computational platform with our 
experimental capabilities enables the rapid design and optimization of new drugs to address 
a wide range of targets for diseases with high unmet need.
We believe that the future of drug discovery lies in integrating predictive sciences, 
biology, and medicinal chemistry to accelerate the path to new medicines. This role 
is an opportunity to be an architect of this paradigm shift and generate 
transformative benefit for patients.

Position Summary: 
Roivant Discovery is looking for chemical physicists/physical chemists to join our 
computational platform team. Working closely with other platform team members, the 
candidate will lead and expand a team to develop and implement computational models 
in molecular physics to enable computation-driven drug discovery. Competitive pay, 
equity, strong perks, and a fun working environment, along with the opportunity to 
do cutting edge science to design better medicines, are all good reasons to join us! 

Responsibilities:  
	Develop and implement models and methods in computational molecular physics,
        including but not limited to 
	Develop accurate and computationally efficient models of molecular interactions,
        including quantum chemistry, force field, and machine-learning models, for small 
        molecules, proteins, and nucleic acids 
	Develop multiscale simulation methods that combine force field, quantum chemistry,
         and machine learning models to enable highly accurate molecular simulations, 
         including binding free energy predictions and protein conformational ensemble predictions  
	Leverage existing biophysical data and collaborate with experimental groups to 
        design experiments and obtain new data to validate the models and simulation methods  
	Develop robust protocols for parameterizing classical force fields and validate 
        such protocols against a wide range of experimental data 
	Collaborate with platform teams to deploy the above models and simulation methods 
        in target evaluation and drug discovery projects to enable or substantially accelerate such efforts 
	Work with experimental groups to validate and benchmark the computational models in drug discovery projects 
 
Required Qualifications:  
	Highly motivated to develop computational methods for discovering better medicines 
	M.S. or Ph.D. in computational physics/chemistry, physical chemistry/chemical physics, applied mathematics, or related fields 
	Strong record of past research accomplishments 
	Extensive past experience in quantum chemistry, force field development, and molecular dynamics simulations 
	Extensive programming experience (C/C++ and Python preferred) 
	Excellent communication skills and strong team player 
 Additional Desirable Qualifications:  
	Experience working with a diverse team on an ambitious project 
	Experience in deep learning and numerical optimization
https://grnh.se/e5d0d1c21us
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