Senior Manager, Applied Physics & Simulation

JR United Kingdom; Nottingham; On-site; GBP 60,000 - 90,000; Full time

Job summary

A renowned biotech company is seeking a Senior Manager for Applied Physics & Simulation to lead and optimize drug discovery through advanced computational methods. The role involves mentoring a team, collaborating with multidisciplinary experts, and applying innovative solutions to achieve groundbreaking scientific results. Candidates should possess in-depth experience in computational physics and be ready to tackle complex challenges in a high-performance environment.

Qualifications

• Extensive industry experience in computational physics.
• Expertise in machine learning applications for physics-based modeling.
• Proven contributions to computational drug discovery.

Responsibilities

• Mentor and lead a team focused on physics-based computational methods.
• Integrate strategies using free energy calculations within drug discovery pipelines.
• Communicate scientific insights to stakeholders, ensuring timely delivery of results.

Skills

Mentoring

Technical Leadership

Collaboration

Problem-Solving

Communication

Education

PhD in Physics or related field

Job description

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Senior Manager, Applied Physics & Simulation, nottingham

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Client:

Barrington James

Location:

nottingham, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Views:

2

Posted:

04.06.2025

Expiry Date:

19.07.2025

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Job Description:

I am working alongside an incredible biotech company looking to grow their current team of computational Physicists who want to drive forward groundbreaking innovations. You will be playing a crucial role in building this team, inspiring and collaborating in multi-matrix environments to increase HITs of drug discovery, striving to achieve greatness!

We are looking for an individual with extensive industry experience to provide unique solutions and strategic methods within the team they join.

Within this role you will be optimizing and developing the physics based computational methods in their drug discovery programs, ensuring the computational methods are used effectively accelerating positive results within identifying new drug candidates.

We are looking for a leader with a variety of expertise within computational physics, computational chemistry, statistical mechanics- providing technical, insightful and impactful strategies when managing the team of scientists.

Key responsibilities are as follows:

• You will mentor and lead a team of scientists with a direct focus in physics-based computational methods
• You will integrate strategies using free energy calculations, physics-based models, integrating these methods into the drug discovery pipelines.
• You should provide technical leadership is statistical mechanics, molecular dynamics, computational chemistry and free energy.
• You will be expected to collaborate with multidisciplinary teams like AI researchers, medicinal chemists and computational chemists.

As a leader, you are expected to communicate scientific insights and provide recommendations to key stakeholders- ensuring your team delivers high-quality scientific results on time and fostering a collaborative / high-performing team environment.

Additional skills:

• Experience working with machine learning applications for physics-based modelling
• High performance computing and cloud-based computing/ simulations
• Expertise visible from scientific publications and contributions within computational drug discovery.

How does this opportunity sound to you? Are you an expert using computational physics methods?

Then this could be the next best opportunity for you!

Following your application, Jay Robins will discuss the opportunity with you in detail. She will happily answer any queries surrounding the opportunity and the potential for career growth. This position is highly popular and has possibility to close prematurely; apply as soon as possible to avoid disappointment.

Please select 'apply', alternatively email [emailprotected] with any further information.