At a Glance
- Tasks: Integrate machine learning with physics-based simulations for drug discovery.
- Company: Collaborative partnership between Genesis Therapeutics and Newcastle University.
- Benefits: 100% home fees covered, tax-free living allowance, and additional project-related funding.
- Other info: Exciting opportunity to work on innovative projects over a 4-year studentship.
- Why this job: Transform drug discovery with cutting-edge technology and make a real impact on healthcare.
- Qualifications: Strong background in computational physics or machine learning required.
The predicted salary is between 19200 - 19200 £ per year.
Award Summary: 100% home fees covered, and a minimum tax‑free annual living allowance of £19,000 (25 UKRI rate). An additional allowance will be provided to contribute towards consumables, equipment, and travel related to the project.
Overview: Machine learning aims to transform the drug discovery landscape through the prediction of potential new therapeutics with unprecedented speed and accuracy. Yet this approach works best when combined with physics‑based modelling, in which the interactions between potential drugs and their target receptor are explicitly modelled at the atomic scale. In our previous work we have shown that classical (force field) models with novel functional forms have the flexibility to model interactions in the condensed phase with higher accuracy than traditional (Lennard‑Jones based) models.
Responsibilities: The student will further incorporate high‑level quantum mechanical data and machine learning to improve the scope of these models such that they accurately model interactions between proteins, small organic molecules and water, at a fraction of the cost of quantum mechanics. They will integrate the workflows into drug discovery pipelines at Newcastle University and/or at the industry partner, and thereby showcase the potential of physics‑based modelling for the design of future therapies.
Eligibility Criteria: Please see the MoSMed website for further details regarding academic eligibility. The studentship covers fees at the Home rate (UK and EU applicants with pre‑settled/settled status and meet the residency criteria). International applicants are welcome to apply but will be required to cover the difference between Home and International fees. International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme.
Number Of Awards: 1
Start Date: 16th September 2024
Award Duration: 4 years
Application Closing Date: 21st May 2024
Sponsor: Genesis Therapeutics/Newcastle University
Supervisors: Dr Daniel Cole and Prof Martin Noble (Newcastle) and Dr Simon Boothroyd (Genesis)
Contact Details: Daniel Cole
PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...] employer: Newcastle University
At Genesis Therapeutics and Newcastle University, we pride ourselves on fostering a collaborative and innovative work environment that empowers PhD students to push the boundaries of drug discovery. With comprehensive funding for home fees and a generous living allowance, alongside opportunities for hands-on experience in cutting-edge research, our students benefit from a supportive culture that prioritises personal and professional growth. Located in the vibrant city of Newcastle, you will enjoy a rich academic community and access to state-of-the-art facilities, making this an exceptional place to advance your career in computational physics and machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...]
✨Tip Number 1
Network like a pro! Reach out to current PhD students or alumni from the programme. They can give you insider info on what the supervisors are looking for and how to stand out.
✨Tip Number 2
Prepare for your interview by brushing up on both machine learning and computational physics. We recommend having a few examples ready that showcase your skills in these areas, especially how they relate to drug discovery.
✨Tip Number 3
Show your passion! When you get the chance to chat with the supervisors, let them know why you're excited about this research. A genuine interest can make a huge difference.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we’re here to help you every step of the way!
We think you need these skills to ace PhD Studentship: Integrating computational physics-based simulation and machine learning with d[...]
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the PhD project. We want to see your passion for integrating computational physics and machine learning, so don’t hold back!
Showcase Relevant Experience:Include any relevant research or projects you've worked on that relate to drug discovery, machine learning, or physics-based modelling. This is your chance to shine, so let us know what you’ve done that makes you a great fit!
Be Clear and Concise:Keep your writing clear and to the point. We appreciate well-structured applications that are easy to read. Avoid jargon unless it’s necessary, and make sure your enthusiasm comes through!
Apply Through Our Website:Don’t forget to submit your application through our official website! It’s the best way to ensure we receive all your materials and can consider you for this exciting opportunity.
How to prepare for a job interview at Newcastle University
✨Know Your Stuff
Make sure you brush up on the latest developments in computational physics and machine learning. Familiarise yourself with the specific models mentioned in the job description, like classical force field models and quantum mechanics. This will show your interviewers that you're genuinely interested and knowledgeable about the field.
✨Showcase Your Skills
Prepare to discuss any relevant projects or research you've done that relates to drug discovery or simulation. Be ready to explain how your experience aligns with the responsibilities of the studentship, especially integrating workflows into drug discovery pipelines. Concrete examples will make a strong impression.
✨Ask Smart Questions
Interviews are a two-way street! Prepare thoughtful questions about the project, the team, and the expectations for the role. This not only shows your enthusiasm but also helps you gauge if this is the right fit for you. For instance, ask about the collaboration between Newcastle University and Genesis Therapeutics.
✨Be Yourself
While it's important to be professional, don't forget to let your personality shine through. The supervisors are looking for someone who fits well within their team. Be honest about your motivations and what excites you about this opportunity. Authenticity can set you apart from other candidates.
